Optimal Power Research Articles

Effective optimal control of a wind turbine system with hybrid energy storage and hybrid MPPT approach

This research paper discusses a wind turbine system and its integration in remote locations using a hybrid power optimization approach and a hybrid storage system. Wind turbine systems’ optimization controllers operate MPPT strategies efficiently, optimizing the system’s overall performance. The proposed approach is HTb(P&O/FLC), which combines the P&O and FLC methods. This innovative hybrid MPPT strategy takes advantage of the two methods. It maximizes the wind power thus minimizing stress on the storage system. For storage, batteries are important in isolated renewable energy systems due the interminent renewable sources. Unfortunately, they have some drawbacks as lower energy density, and limited charge-discharge. Besides, supercapacitors (SCs) have advantages as quick charging and discharging and long cycle life and some drawbacks as lower energy density and specific voltage requirements. This paper proposes a hybrid storage system combining batteries and SCs. To manage the two storage technologies, an effective algorithm isproposed focusing on managing their storage systems. The findings indicate that the proposed strategy effectively maintains the state of charge (SOC) of both the batteries and SCs in the intended limits. Simulation under MATLAB and Real-time with RT-LAB were carried out to validate the proposed control approach for the wind turbine system.

Influence of radio-frequency magnetron sputtering power on electrical characteristics and positive bias stress stability of indium tin zinc oxide thin-film transistors

Abstract As the pursuit of high-quality display panels intensifies, the importance of high-performance thin-film transistors (TFTs) becomes increasingly prominent. Indium tin zinc oxide (ITZO) TFTs, as one of the promising directions for high-performance metal oxide TFTs, impose high demands on their performance and reliability. In this study, the performance and stability of ITZO TFTs fabricated under different radio-frequency magnetron sputtering power conditions were systematically tested and investigated. After sputtering power optimization, the field effect mobility of ITZO TFTs reaches 29.68 cm²/V·s, with a steep sub-threshold swing of 0.17 V/decade, near-zero threshold voltage, and good stability. Using atomic force microscopy and X-ray photoelectron spectroscopy characterization techniques, the thin films were analyzed to elucidate the relationship between sputtering power variations and oxygen vacancies.

Modelling and optimisation of solar photovoltaic power using response surface methodology

Modelling and optimisation of solar photovoltaic power using response surface methodology

Comprehensive analysis and optimization of an efficient combined power and refrigeration cycle suitable for recovering low/mid-grade waste flue gas

To effectively recover low/mid-grade waste flue gas and provide larger refrigeration capacity, an efficient combined power/refrigeration cycle only using ammonia water as the working fluid is proposed and studied. Parametric analysis is conducted from the perspectives of thermodynamics, exergoeconomics, and environment, the results demonstrate that the low/mid-grade waste flue gas within a wide range of temperature can be sufficiently recovered by the proposed cycle, and the cycle performance is significantly affected by the turbine inlet pressure, work/basic concentration and separation temperature of work solution. Based on the heat source condition of 300°C/10 kg·s-1, the results of single-objective optimization show that the maximum effective exergy efficiency, reduction amount of CO2 emission and waste heat recovery ratio that can be achieved are 0.5989, 262 kg/h and 0.97 respectively, and the achievable minimum unit cost of produced exergy is 8.432 $·GJ-1. Besides, the multi-objective optimization indicates that the optimal comprehensive thermodynamic efficiency is 0.5458 with net power output of 372.5 kW and larger refrigeration capacity of 545.7 kW, and the associated effective exergy efficiency, unit cost of produced exergy, reduction amount of CO2 emission and waste heat recovery ratio are 0.5701, 9.187 $/GJ, 258.3 kg/h and 0.9574, respectively.

Forecasting Power Consumption in the DSL Access Network System based on Network Activities by using Machine Learning

The widespread telecommunication networks and Internet services like Digital Subscriber Line (DSL) access has contributed to the growing demand of electricity. With environmental consciousness arising from non-renewable energy sources, coupled with rising demand, industries need to implement green strategies. This research focuses on predicting power consumption in DSL access networks using machine learning approaches. With an emphasis on Very High Bit Rate Digital Subscriber Line 2 (VDSL2) technology, the study investigates DSL modem’s power usage under an ideal and a bridge tap faulty conditions of copper cable along distances ranging from 100 to 1000 meters. The different levels of network activities are generated by IxChariot software and Pearson’s correlation techniques are used to analyse the relationship between power consumption and network activities. Combining the Train-Valid-Test and Random Forest algorithms, a predictive Machine Learning model is developed to forecast the power consumption based on relevant variables. The results show that power consumption tends to increase as the network activities are heavier and the proposed model presents a low minimum absolute error as a good model to forecast the power. It may aid Internet service providers to predict the optimum power limit in the network systems and pursue further investigation on how to reduce electricity use in the telecommunication field.

Designing RNS-based FIR filter with Optimal area, Delay, and Power via the use of Swift Adders and Swift Multipliers

Based on the Residue Number System (RNS), Finite Impulse Response filters have gained prominence in digital signal processing due to their efficiency in handling complex computations. This work presents a comprehensive analysis on optimizing area, delay, and power in the FIR filter by exploring different adders and multipliers. Three prominent adders, namely Ripple Carry Adder, Kogge Stone Adder, and Proposed Adder, are evaluated for their impact on area and delay. The choice of adder influences the overall performance of the FIR filter, and a careful selection is made based on the trade-offs between area and delay. Furthermore, various multipliers, including Booth, Baugh-Wooley, Braun, and Array, are compared in terms of their efficiency in power consumption. Multipliers contribute significantly to the overall power consumption, and the analysis involves selecting the most suitable multiplier for achieving the desired power optimization. The various arrangements of swift adders and swift multipliers were suggested and executed in the Finite Impulse Response (FIR) filter and then the RNS algorithm was applied to it. The comparison of 8-tap 8-bit of the proposed adder with the array multiplier shows that the area is reduced by 52.70% with other combinations and by comparing the RCA adder with the array multiplier, the critical path delay is reduced by 38.51% and the maximum frequency is produced by the KSA adder with Braun multiplier which is increased by 29.78% with other combinations.

Improved fractional order control with virtual inertia provision methodology for electric vehicle batteries in modern multi-microgrid energy systems

Providing inertia in modern electrical power grids has become a highly demanding task to mitigate reduced power system inertia of renewable energy sources (RES) based power grids. To mitigate the reduced inertia-related problems and consequences, this paper presents improved FOCs for achieving load frequency control (LFC) and the virtual inertia control (VIC) provision of electric vehicles (EVs) lithium-ion batteries. The proposed method includes a twofold contribution using a new hybrid FOC method and an optimum control parameter design method. An innovative hybrid controller is proposed by merging the characteristics of FOPID and TID methods with a fractional filtering stage, leading to hybrid tilt plus FOC integrator-derivative terms. From the VIC side, the TID is proposed to provide a fast response to lithium-ion batteries of the already available EVs. Furthermore, a metaheuristic-based design method is proposed to simultaneously determine the best control parameters set and to facilitate the design procedures using the recently presented powerful growth optimization algorithm (GO). The two interconnected areas case study with high-RES penetration is suggested for testing and investigating the new proposed controller. The results include performance evaluations and comparisons at various expected model uncertainties, renewable energy intermittency, and parametric variations. For instance, the proposed VIC method has a minimized ISE after 28 iterations of 0.001 compared to 0.003 with conventional VIC, and 0.004 without VIC. Thence,the proposed controller achieves more accurate frequency tracking with reduced deviations and fluctuations and exhibits greater robustness compared to previous controllers used in all case studies.

Optimization of D2D emergency communication resources for UAV relay based on DA-STD3

The allocation of communication resources is extremely important for the device-to-device (D2D) communication network with unmanned aerial vehicle (UAV) relays, but this field currently mainly focuses on the communication between UAVs and ground devices. While neglecting the comprehensive optimization of air-to-ground communication and internal ground communication. This paper studies the optimization problem of communication for ground D2D devices based on UAV relays in urban environment. We study the comprehensive optimization considering the air-to-ground communication and the mobile ground communication devices. We construct the D2D communication neighbor table, and the terrestrial D2D devices will sort and select the best D2D link for pairing based on the Euclidean distance and gain set around them. We construct a communication model for this scenario and propose an improved Dual-Actor Network Softmax Twin Delayed Deep Deterministic Policy Gradient algorithm based on deep reinforcement learning. We perform transmit power optimization to address the interference caused by D2D communication using frequency division multiplexing technology. By designing the reward function and training mechanism, we can achieve good power control and reduce communication interference. The results of the simulation analysis performed in this scenario show an effective reduction in communication latency and an increase in D2I (device-to-infrastructure) channel capacity, D2D connectivity success, and energy efficiency. The feasibility and effectiveness of the algorithm is verified by comparing the baseline algorithm.

Facile synthesis of CuCo2O4@CdS nanoheterostructure for asymmetric supercapacitor

The abundant active sites, high theoretical capacitance, and cost-effective synthesis of transition metal oxides@sulfides (TMOs@TMSs) have made them a highly desirable choice as active electrode materials for supercapacitors. This study demonstrated the successful fabrication of a CuCo2O4@CdS nanoheterostructure electrode using a simple and economical co-precipitation method The morphology, physicochemical properties, and electrochemical behavior of this electrode were subsequently studied. The synthesized CuCo2O4@CdS nanoheterostructure was characterized using Infrared Fourier-transform spectroscopy, X-ray diffraction, Raman spectroscopy, energy dispersive X-ray spectroscopy, X-ray photoelectron spectroscopy, scanning electron microscopy, High-resolution transmission electron microscopy, N2 adsorption/desorption isotherms and atomic force microscopy. The electrochemical performance was studied using cyclic voltammetry, impedance spectroscopy, and galvanostatic charge-discharge (GCD) measurements with a 6 Molar KOH aqueous electrolytic solution. Results from powder X-ray diffraction pattern and energy dispersive X-ray revealed the formation of CuCo2O4@CdS nanoheterostructure. Furthermore, SEM images showed the formation of more spherical structures in nanoheterostructure. The results of optical measurements of thin films were analyzed, which showed an increase in the energy gap (Eg) of CuCo2O4 nanoparticles when combined with cadmium sulfide (CdS). Additionally, the intensity of photoluminescence of the CuCo2O4@CdS nanoheterostructure was lower compared to the pure nanoparticles. Moreover, the newly synthesized CuCo2O4@CdS nanoheterostructure showed a specific capacitance of 257.5 F g−1 at a current density of 0.5 A g−1 via a three-electrode galvanostatic charge-discharge system, respectively. In the asymmetric device, the supercapacitor yielded an optimum energy density and power density of 19.6 Wh kg−1 and 700 W kg−1 at 1 A g−1 current density, respectively. Although the capacitance retention was maintained at nearly 85.2 % after 4000 cycles at a current density of 3 A g−1.

An amended low-cost indirect MPPT strategy with a PID controller for boosting PV system efficiency

Operating a photovoltaic (PV) array at its optimum power point (MPP) despite fluctuating weather conditions is challenging. However, tracking this point enhances system efficiency and energy yields. Various maximum power point tracker (MPPT) techniques have been developed, differing in convergence speed, tracking efficiency, and implementation complexity. The Incremental Conductance (IC) strategy is popular due to its simplicity and reliability, but its fixed perturbation step size results in low tracking efficiency, MPP fluctuation, high power loss, and poor performance. This study proposes an improved IC (IMP-IC) method using indirect control based on a PID controller with an adjustable step size. The IMP-IC approach is simple, cost-effective, and implementable using a low-cost ATmega328 microcontroller on the Arduino Uno board.Simulations in the Proteus environment compared its performance to other MPPT techniques like conventional IC, zone voltage (ZV), fuzzy logic (FL), and novel adjustable step InC (NAS-INC). Results under various weather conditions, including rapid changes and EN50530 testing standards, indicate the IMP-IC approach outperforms existing methods. Specifically, it achieved an average tracking time of 0.12 seconds and tracking efficiencies of 99.40 % (static) and 99.88 % (dynamic). The IMP-IC method showed negligible power losses and fluctuations, a quick convergence speed and significantly lower implementation complexity. These attributes position the IMP-IC method as an effective solution for enhancing the reliability and performance of PV systems.

Impact of microwave pretreatment on yield, quality and lipid composition of Acer truncatum Bunge seed oil

This study is to optimize microwave pretreatment of Acer truncatum seeds to enhance quality and production of Acer truncatum seed oil (ATSO). The optimum microwave power, microwave-treatment time, as well as microwave-treatment temperature were determined through single-factor and orthogonal experiments, and they were 600 W, 5 min, and 50℃, respectively, resulting in an ATSO yield of 35.2 %. The lipid components of ATSO extracted from non-microwave-treated as well as microwave-treated A. truncatum seeds were qualitatively and quantitatively evaluated by lipidomic analysis. Through liquid chromatography–tandem mass spectrometry (LC-MS/MS) analysis, 2005 lipid molecules belonging to 42 lipid subclasses were specified. The outcomes of lipidomic analysis demonstrated the microwave pretreatment of A. truncatum seeds not only increased levels of beneficial lipids in ATSO but also changed lipid composition of the oil, potentially enhancing the positive health effects of the oil. This research brings a scientific basis for improving the yield as well as quality of ATSO and helps in understanding lipid composition and health benefits of the oil, promoting the utilization of this valuable natural resource.

Parameter Adaptive Contrastive Hashing for multimedia retrieval

With the emergence of massive amounts of multi-source heterogeneous data on the Internet, quickly retrieving effective information from this extensive data has become a hot research topic. Due to the efficiency and speed of hash learning methods in multimedia retrieval, they have become a mainstream method for multimedia retrieval. However, unsupervised multimedia hash learning methods still face challenges with the difficulties of tuning due to the excessive number of hyperparameters and the lack of precise guidance on semantic similarity. To address these problems, we propose a Parameter Adaptive Contrastive Hashing (PACH) method for multimedia retrieval. The Fast Parameter Adaptive (FPA) module, combined with the powerful space exploration and dynamic optimization capabilities of reinforcement learning, designs a hot-plugging multimedia hashing method parameter adaptation module to solve for an approximate optimal combination of parameters. The Multimedia Contrastive Hashing (MCH) module comprehensively explores intra- and inter-modal semantic consistency of multimodal data, enriching the cross-modal semantic information of the hash codes. Comprehensive experiments were designed and compared with the latest hash learning methods, verifying the effectiveness and superiority of the PACH method. The code is available at https://github.com/YunfeiChenMY/PACH.

Distributionally Robust Optimal Scheduling of Hybrid Ship Microgrids Considering Uncertain Wind and Wave Conditions

A hybrid ship uses integrated generators, an energy storage system (ESS), and photovoltaics (PV) to match its propulsion and service loads, and together with optimal power and voyage scheduling, this can lead to a substantial improvement in ship operation cost, ensuring compliance with the environmental constraints and enhancing ship sustainability. During the operation, significant uncertainties such as waves, wind, and PV result in considerable speed loss, which may lead to voyage delays and operation cost increases. To address this issue, a distributionally robust optimization (DRO) model is proposed to schedule power generation and voyage. The problem is decoupled into a bi-level optimization model, the slave level can be solved directly by commercial solvers, the master level is further formulated as a two-stage DRO model, and linear decision rules and column and constraint generation algorithms are adopted to solve the model. The algorithm aims at minimizing the operation cost, limiting greenhouse gas (GHG) emissions, and satisfying the technical and operational constraints considering the uncertainty. Extensive simulations demonstrate that the expected total cost under the worst-case distribution is minimized, and compared with the conventional robust optimization methods, some distribution information can be incorporated into the ambiguity sets to generate fewer conservative results. This method can fully ensure the on-time arrival of hybrid ships in various uncertain scenarios while achieving expected operation cost minimization and limiting greenhouse gas (GHG) emissions.

Surrogate-assisted fully-informed particle swarm optimization for high-dimensional expensive optimization

Surrogate-Assisted Evolutionary Algorithms (SAEAs) have been proven to be powerful optimization tools for tackling Expensive Optimization Problems (EOPs) where a limited number of function evaluations are available. However, many SAEAs are only designed for low- or medium-dimensional EOPs. Existing SAEAs are challenging to address High-dimensional EOPs (HEOPs) owing to the curse of dimensionality and lack of powerful exploitation capacity. To tackle HEOPs efficiently, a Surrogate-Assisted Fully-informed Particle Swarm Optimization (SA-FPSO) algorithm is proposed in this paper. Firstly, a generation-based Social Learning-based PSO (SLPSO) is adopted to explore the whole decision space with the help of the global surrogate model. Secondly, the fully-informed search scheme is incorporated into the framework of SLPSO to improve its exploitation capacity in the surrogate-assisted search environment. Thirdly, a local space identification strategy is proposed to determine the search range for the local surrogate-assisted search. Seven commonly used expensive benchmark functions with dimensions ranging from 30D to 300D are used to verify the performance of SA-FPSO for HEOPs. Experiment results indicate that SA-FPSO obtains superior performance over several state-of-the-art SAEAs both in terms of convergence speed and solution accuracy.

An Optimized Strategy for the Integration of Photovoltaic Systems and Electric Vehicles into the Real Distribution Grid

The increasing spread of photovoltaic systems for private households (PVs) and electric vehicles (EVs) in order to reduce carbon emissions significantly impacts operation conditions in existing distribution networks. Variable and unpredictable PVs can stress distribution network operation, mainly manifested in voltage violations during the day. On the other hand, variable loads such as EV chargers which have battery storage in their configuration have the ability of storying a surplus energy and, if it is necessary, support a distribution network with energy, commonly known as vehicle-to-grid concept (V2G), to help voltage stability network enhancement. This paper proposes an optimal power flow (OPF)-based model for EV charging to minimize power exchange between the superior-10 kV grid and the observed distribution feeder. The optimization procedure is realized using the co-simulation approach that connects power flow analysis software and optimization method. Three different scenarios are observed and analysed. The first scenario is referred to as a base case without optimization. The second and third scenarios include optimal EV charging and discharging patterns under different constraints. To test the optimization model, a 90-bus unbalanced distribution feeder modelled based on real-life examples is used. The obtained results suggest that this optimization model does not only significantly reduce the power exchange between an external network and the distribution feeder but also improves voltage stability and demand curve in the distribution feeder.

Numerical model of a top-blown rotary converter preheating and charge heating with an oxy-fuel burner

Background The present work is conducted in the framework of the SisAl Pilot EU project, which aims to optimize silicon production in Europe by recycling materials and using carbon-emission-friendly technology. Silicon production experiments were conducted on laboratory and pilot scales in different types of furnaces, including top-blown rotary converters (TBRC) used as chemical reactors for molten slag-metal mixtures. In addition to experimental work, process optimization also relies on numerical modelling. Methods In this study, COMSOL Multiphysics® was used for the numerical testing of a new thermal design of TBRC by simulating its preheating and charge heating owing to an external heat source provided by an oxy-fuel burner. Results and conclusions The risk of slag solidification in TBRC during the aluminothermic reduction of silica was assessed. The model predicts that, with a useful burner power of 600 kW, the empty TBRC can be preheated to 1650°C in less than 30 min. Based on this model, the optimum burner power for maintaining the TBRC charge in a liquid state was determined. The influence of the TBRC inclination angle and its rotation frequency was studied numerically.

Study on two‐stage optimal scheduling of DC distribution networks considering flexible load response

AbstractDue to the aim of developing sustainable energy systems, promoting the large‐scale accommodation of distributed renewable energy sources (DRESs) and flexible loads in DC distribution networks (DCDNs) is significant. The uncertainty of DRESs and the insufficient use of flexible loads pose a considerable challenge to the economical and safe operation of DCDNs. To address the challenge, this paper puts forward a two‐stage optimal scheduling model for the DCDNs considering flexible load response. The proposed model realises joint economic optimisation and reactive power optimisation, which is solved by the hybrid NSGAII‐MOPSO algorithm and the CPLEX. The performance of the proposed model in the modified Institute of Electrical and Electronics Engineers 33‐node system with the DCDNs is validated under different scenarios. The hybrid NSGAII‐MOPSO performs better in obtaining the Pareto front than the NSGA‐II and MOPSO individually. Compared to the traditional scheduling model, the proposed model can realise the power coordination of the flexible loads and energy storage systems to reduce the negative impact of uncertainty of DRESs while decreasing operating costs and carbon emissions by 3.94% and 36.4%. In addition, the proposed model can alleviate the network losses and ensure the node voltage for the DCDNs. Hence, the efficiency of the proposed model has been confirmed.

An Optimized Multi-Level Control Method for Wireless Power Transfer System Using the Particle Swarm Optimization Algorithm

A Wireless Power Transfer (WPT) system, known for its contactless power delivery, is extensively used for power supply in spacecraft applications. Achieving efficient and stable power transfer necessitates the integration of DC/DC converters on both the primary and secondary sides of WPT systems for power conversion and control. Traditional efficiency optimization methods primarily focus on impedance matching within the wireless power resonance network, often neglecting the overall efficiency optimization of multi-stage DC-DC and WPT systems. This oversight results in suboptimal overall system efficiency despite optimal efficiency in the wireless transmission segment. Additionally, the time-varying nature of mutual inductance and load parameters during power transmission in WPT systems presents challenges for maximum efficiency tracking and power control. This paper introduces a multi-level coordinated control efficiency optimization method for WPT systems utilizing the particle swarm optimization (PSO) algorithm. This method takes into account the transmission losses across all power conversion units within the WPT system, establishing a mathematical model for the joint optimization of overall system transmission efficiency and power. The PSO algorithm is then employed to solve this optimization model using estimated mutual inductance and load values. By adjusting the DC/DC converters on both sides, the method ensures optimal overall system efficiency and consistent power transmission. Experimental results indicate that under varying load and mutual inductance conditions, a Series–Series (SS) compensated WPT system using this method achieves a 200 W power output with maximum efficiency tracking, a power output error of 0.63%, and an average transmission efficiency of 86.2%. This demonstrates superior power transmission stability and higher efficiency compared to traditional impedance matching methods.

Experimental study on the low-temperature preheating performance of positive-temperature-coefficient heating film in the prismatic power battery module

The performance of a power battery directly affects the thermal safety performance of the vehicle. Aiming at the improvement of thermal safety of lithium-ion batteries under low temperature condition, this study focuses on the effect of the positive-temperature-coefficient (PTC) heating film on the heating performance of batteries through experimental testing. First, the side and bottom of the cell were heated, and the heat transfer path and mode of the single cell were analyzed. The effects of different power densities and geometric positions on the heating effect were studied by testing the heating time and temperature consistency. Second, for the battery module, a low-temperature heating thermal management heat transfer model was built to analyze the heating mechanism of the heating film under different heating power densities. Finally, the results of these studies were synthesized to obtain the optimal heating power density. The results show that for the cell, the optimum power density of side heating and bottom heating is 0.5 W /cm2 and 0.4 W /cm2, respectively. The time required for side heating from −20 °C to 10 °C is 730 s lower than that of bottom heating, and the maximum temperature difference is 1.885 °C lower. For battery modules, a power density of 0.5 W/cm2 was appropriate for both bottom and side heating methods. Due to the existence of heat conduction between battery packs, the battery modules will be quickly and evenly preheated. Under the optimal power density, the time length for side heating from –20 °C to 10 °C was 1459 s, and the maximum temperature difference was 6.32 °C; bottom heating time required slightly more time (1783 s), and the maximum temperature difference was 6.221 °C. Side heating can be beneficial for the rapid preheating of the battery module. This study will contribute to improving the performance and safety of batteries in cold environments.

Distributed photovoltaic reactive power control strategy based on improved multiobjective particle swarm algorithm

AbstractDistributed power supply access to the distribution network, although it can effectively support the band voltage, will also cause problems such as voltage overruns at the point of grid connection and large network losses, so this paper establishes a reactive power optimization model containing three objectives: network loss, voltage fluctuation rate, and static reactive power generator (SVG) installation capacity in distributed photovoltaic power generation scenarios by taking advantage of the characteristics of SVG that both absorb and send out reactive power. A multiobjective particle swarm algorithm with an adaptive grid and roulette mechanism is introduced to ensure the uniformity and diversity of the Pareto boundaries under the constraint that the output of each device does not exceed the constraints, and to obtain the optimal set of solutions capable of coping with the stochastic fluctuations of distributed power sources. When the algorithm is compared with three other algorithms, such as nondominated sorting genetic algorithm‐II, the results show that it reduces the network loss by about 25% and significantly improves the voltage fluctuation rate.