Economic Power Dispatching from Distributed Generations: Review of Optimization Techniques

In the increasingly decentralized energy environment,economical power dispatching from distributed generations(DGs)is crucial to minimizing operating costs,optimizing resource utilization,and guaranteeing a consistent and sustainable supply of electricity.A comprehensive review of optimization techniques for economic power dispatching from distributed generations is imperative to identify the most effective strategies for minimizing operational costs while maintaining grid stability and sustainability.The choice of optimization technique for economic power dispatching from DGs depends on a number of factors,such as the size and complexity of the power system,the availability of computational resources,and the specific requirements of the application.Optimization techniques for economic power dispatching from distributed generations(DGs)can be classified into two main categories:(i)Classical optimization techniques,(ii)Heuristic optimization techniques.In classical optimization techniques,the linear programming(LP)model is one of the most popular optimization methods.Utilizing the LP model,power demand and network constraints are met while minimizing the overall cost of generating electricity from DGs.This approach is efficient in determining the best DGs dispatch and is capable of handling challenging optimization issues in the large-scale system including renewables.The quadratic programming(QP)model,a classical optimization technique,is a further popular optimization method,to consider non-linearity.The QP model can take into account the quadratic cost of energy production,with consideration constraints like network capacity,voltage,and frequency.The metaheuristic optimization techniques are also used for economic power dispatching from DGs,which include genetic algorithms(GA),particle swarm optimization(PSO),and ant colony optimization(ACO).Also,Some researchers are developing hybrid optimization techniques that combine elements of classical and heuristic optimization techniques with the incorporation of droop control,predictive control,and fuzzy-based methods.These methods can deal with large-scale systems with many objectives and non-linear,non-convex optimization issues.The most popular approaches are the LP and QP models,while more difficult problems are handled using metaheuristic optimization techniques.In summary,in order to increase efficiency,reduce costs,and ensure a consistent supply of electricity,optimization techniques are essential tools used in economic power dispatching from DGs.

Role of Examples and Interpretation of Results in Developing Multi-Objective Optimization Techniques

The paper evaluates the suitability of examples used in developing averaging techniques of multi-objective optimization (MOO). Most of the examples used for proposing these techniques were not suitable. The results of these examples have also not been interpreted correctly. An appropriate example has also been solved with existing and improved averaging techniques of multi-objective optimization.

Optimal Allocation of a Hybrid Wind Energy-Fuel Cell System Using Different Optimization Techniques in the Egyptian Distribution Network

This paper presents an optimal proposed allocating procedure for hybrid wind energy combined with proton exchange membrane fuel cell (WE/PEMFC) system to improve the operation performance of the electrical distribution system (EDS). Egypt has an excellent wind regime with wind speeds of about 10 m/s at many areas. The disadvantage of wind energy is its seasonal variations. So, if wind power is to supply a significant portion of the demand, either backup power or electrical energy storage (EES) system is needed to ensure that loads will be supplied in reliable way. So, the hybrid WE/PEMFC system is designed to completely supply a part of the Egyptian distribution system, in attempt to isolate it from the grid. However, the optimal allocation of the hybrid units is obtained, in order to enhance their benefits in the distribution networks. The critical buses that are necessary to install the hybrid WE/ PEMFC system, are chosen using sensitivity analysis. Then, the binary Crow search algorithm (BCSA), discrete Jaya algorithm (DJA) and binary particle swarm optimization (BPSO) techniques are proposed to determine the optimal operation of power systems using single and multi-objective functions (SOF/MOF). Then, the results of the three optimization techniques are compared with each other. Three sensitivity factors are employed in this paper, which are voltage sensitivity factor (VSF), active losses sensitivity factor (ALSF) and reactive losses sensitivity factor (RLSF). The effects of the sensitivity factors (SFs) on the SOF/MOF are studied. The improvement of voltage profile and minimizing active and reactive power losses of the EDS are considered as objective functions. Backward/forward sweep (BFS) method is used for the load flow calculations. The system load demand is predicted up to year 2022 for Mersi-Matrouh City as a part of Egyptian distribution network, and the design of the hybrid WE/PEMFC system is applied. The PEMFC system is designed considering simplified mathematical expressions. The economics of operation of both WE and PEMFC system are also presented. The results prove the capability of the proposed procedure to find the optimal allocation for the hybrid WE/PEMFC system to improve the system voltage profile and to minimize both active and reactive power losses for the EDS of Mersi-Matrough City.

Optimization Techniques for GPU-Based Parallel Programming Models in High-Performance

This study embarks on a comprehensive examination of optimization techniques within GPU-based parallel programming models,pivotal for advancing high-performance computing(HPC).Emphasizing the transition of GPUs from graphic-centric processors to versatile computing units,it delves into the nuanced optimization of memory access,thread management,algorithmic design,and data structures.These optimizations are critical for exploiting the parallel processing capabilities of GPUs,addressingboth the theoretical frameworks and practical implementations.By integrating advanced strategies such as memory coalescing,dynamic scheduling,and parallel algorithmic transformations,this research aims to significantly elevate computational efficiency and throughput.The findings underscore the potential of optimized GPU programming to revolutionize computational tasks across various domains,highlighting a pathway towards achieving unparalleled processing power and efficiency in HPC environments.The paper not only contributes to the academic discourse on GPU optimization but also provides actionable insights for developers,fostering advancements in computational sciences and technology.

Crowd evacuation simulation model with soft computing optimization techniques:a systematic literature review

Crowd evacuation simulation is an essential element when it comes to planning and preparation in evacuation management.This paper presents the survey based on systematic literature review(SLR)technique that aims to identify the crowd evacuation under microscopic model integrated with soft computing technique from previous works.In the review process,renowned databases were searched to retrieve the primary articles and total 38 studies were thoroughly studied.The researcher has identified the potential optimization factors in simulating crowd evacuation and research gaps based on acquired issues,limitation and challenges in this domain.The results of this SLR will serve as a guideline for the researchers that have same interest to develop better and effective crowd evacuation simulation model.The future direction from this SLR also suggests that there is a potential to hybrid the model with softcomputing optimization focusing on latest nature-inspired algorithms in improving the crowd evacuation model.

Enhanced Cuckoo Search Optimization Technique for Skin Cancer Diagnosis Application

Skin cancer segmentation is a critical task in a clinical decision support system for skin cancer detection.The suggested enhanced cuckoo search based optimization model will be used to evaluate several metrics in the skin cancer pic-ture segmentation process.Because time and resources are always limited,the proposed enhanced cuckoo search optimization algorithm is one of the most effec-tive strategies for dealing with global optimization difficulties.One of the most significant requirements is to design optimal solutions to optimize their use.There is no particular technique that can answer all optimization issues.The proposed enhanced cuckoo search optimization method indicates a constructive precision for skin cancer over with all image segmentation in computerized diagnosis.The accuracy of the proposed enhanced cuckoo search based optimization for melanoma has increased with a 23%to 29%improvement than other optimization algorithm.The total sensitivity and specificity attained in the proposed system are 99.56%and 99.73%respectively.The proposed method outperforms by offering accuracy of 99.26%in comparisons to other conventional methods.The proposed enhanced optimization technique achieved 98.75%,98.96%for Dice and Jaccard coefficient.The model trained using the suggested measure outperforms those trained using the conventional method in the segmentation of skin cancer picture data.

Advanced Optimized Anomaly Detection System for IoT Cyberattacks Using Artificial Intelligence

While emerging technologies such as the Internet of Things(IoT)have many benefits,they also pose considerable security challenges that require innovative solutions,including those based on artificial intelligence(AI),given that these techniques are increasingly being used by malicious actors to compromise IoT systems.Although an ample body of research focusing on conventional AI methods exists,there is a paucity of studies related to advanced statistical and optimization approaches aimed at enhancing security measures.To contribute to this nascent research stream,a novel AI-driven security system denoted as“AI2AI”is presented in this work.AI2AI employs AI techniques to enhance the performance and optimize security mechanisms within the IoT framework.We also introduce the Genetic Algorithm Anomaly Detection and Prevention Deep Neural Networks(GAADPSDNN)sys-tem that can be implemented to effectively identify,detect,and prevent cyberattacks targeting IoT devices.Notably,this system demonstrates adaptability to both federated and centralized learning environments,accommodating a wide array of IoT devices.Our evaluation of the GAADPSDNN system using the recently complied WUSTL-IIoT and Edge-IIoT datasets underscores its efficacy.Achieving an impressive overall accuracy of 98.18%on the Edge-IIoT dataset,the GAADPSDNN outperforms the standard deep neural network(DNN)classifier with 94.11%accuracy.Furthermore,with the proposed enhancements,the accuracy of the unoptimized random forest classifier(80.89%)is improved to 93.51%,while the overall accuracy(98.18%)surpasses the results(93.91%,94.67%,94.94%,and 94.96%)achieved when alternative systems based on diverse optimization techniques and the same dataset are employed.The proposed optimization techniques increase the effectiveness of the anomaly detection system by efficiently achieving high accuracy and reducing the computational load on IoT devices through the adaptive selection of active features.

On recommendation-aware content caching for 6G:An artificial intelligence and optimization empowered paradigm

Recommendation-aware Content Caching(RCC)at the edge enables a significant reduction of the network latency and the backhaul load,thereby invigorating ubiquitous latency-sensitive innovative services.However,the effectiveness of RCC strategies is highly dependent on explicit information as regards subscribers’content request patterns,the sophisticated caching placement policy,and the personalized recommendation tactics.In this article,we investigate how the potentials of Artificial Intelligence(AI)and optimization techniques can be harnessed to address those core issues and facilitate the full implementation of RCC for the upcoming intelligent 6G era.Towards this end,we first elaborate on the hierarchical RCC network architecture.Then,the devised AI and optimization empowered paradigm is introduced,whereas AI and optimization techniques are leveraged to predict the users’content preferences in real-time situations with the assistance of their historical behavior data and determine the cache pushing and recommendation decision,respectively.Through extensive case studies,we validate the effectiveness of AI-based predictors in estimating users’content preference and the superiority of optimized RCC policies over the conventional benchmarks.At last,we shed light on the opportunities and challenges in the future.

Efficient Approach for Resource Allocation in WPCN Using Hybrid Optimization

The recent aggrandizement of radio frequency(RF)signals in wireless power transmission combined with energy harvesting methods have led to the replacement of traditional battery-powered wireless networks since the blooming RF technology provides energy renewal of wireless devices with the quality of service(QoS).In addition,it does not require any unnecessary alterations on the transmission hardware side.A hybridized global optimization technique uniting Global best and Local best(GL)based particle swarm optimization(PSO)and ant colony optimization(ACO)is proposed in this paper to optimally allocate resources in wireless powered communication networks(WPCN)through coordinated operation of communication groups,in which the wireless energy transfer and information sharing take place concomitantly by the aid of a cooperative relay positioned in between the communicating groups.The designed algorithm assists in minimizing power consumption and maximizes the weighted sum rate at the end-user side.Thus the principal target of the system is coordinated optimization of energy beamforming along with time and energy allocation to reduce the total energy consumed combined with assured information rates of the communication groups.Numerical outputs are presented to manifest the proposed system’s performance to verify the analytical results via simulations.

An Optimized Deep Learning Approach for Improving Airline Services

The aviation industry is one of the most competitive markets. Themost common approach for airline service providers is to improve passengersatisfaction. Passenger satisfaction in the aviation industry occurs whenpassengers’ expectations are met during flights. Airline service quality iscritical in attracting new passengers and retaining existing ones. It is crucialto identify passengers’ pain points and enhance their satisfaction with theservices offered. The airlines used a variety of techniques to improve servicequality. They used data analysis approaches to analyze the passenger pointdata. These solutions have focused simply on surveys;consequently, deeplearningapproaches have received insufficient attention. In this study, deepneural networks with the adaptive moment estimation Adam optimizationalgorithm were applied to enhance classification performance. In previousstudies, the quality of the dataset has been ignored. The proposed approachwas applied to the airline passenger satisfaction dataset from the Kagglerepository. It was validated by applying artificial neural networks (ANNs),random forests, and support vector machine techniques to the same dataset. Itwas compared with other research papers that used the same dataset and had asimilar problem. The experimental results showed that the proposed approachoutperformed previous studies. It has achieved an accuracy of 99.3%.

A comprehensive review of recent advancements and developments in heat exchanger network synthesis techniques

The rapid development of computational technology and the increasing energy demand have improved heat exchanger network(HEN)synthesis.The HEN synthesis involves several optimizations of matches,distributions of heat loads,and stream splitting of heat units.Thus,obtaining good results at high efficiency has been the main standard for evaluating the techniques in the research area of HEN synthesis.This paper first summarizes and analyzes the main contributions of the existing HEN synthesis techniques.To compare related data quantitively,information on ten typical cases is presented in this paper.Furthermore,recently improved solutions for commonly encountered existing literature cases demonstrate the evolution and competition trends in the field of HEN synthesis.The comparison data presented in this paper not only provide a useful reference for future research but also present the optimization directions.Based on the findings of this study,it is noted that there is still a large room for improvement,and current approaches are incapable of dealing with all HEN cases.Moreover,it is still difficult to escape a local optimum and overcome structural constraints when seeking the global optimum.As a follow-up to the current work,the parallel computing mode and adaptively coordinating the ratio of global and local searching abilities are major development trends for future investigation.

Energy Theft Identification Using Adaboost Ensembler in the Smart Grids

One of the major concerns for the utilities in the Smart Grid(SG)is electricity theft.With the implementation of smart meters,the frequency of energy usage and data collection from smart homes has increased,which makes it possible for advanced data analysis that was not previously possible.For this purpose,we have taken historical data of energy thieves and normal users.To avoid imbalance observation,biased estimates,we applied the interpolation method.Furthermore,the data unbalancing issue is resolved in this paper by Nearmiss undersampling technique and makes the data suitable for further processing.By proposing an improved version of Zeiler and Fergus Net(ZFNet)as a feature extraction approach,we had able to reduce the model’s time complexity.To minimize the overfitting issues,increase the training accuracy and reduce the training loss,we have proposed an enhanced method by merging Adaptive Boosting(AdaBoost)classifier with Coronavirus Herd Immunity Optimizer(CHIO)and Forensic based Investigation Optimizer(FBIO).In terms of low computational complexity,minimized over-fitting problems on a large quantity of data,reduced training time and training loss and increased training accuracy,our model outperforms the benchmark scheme.Our proposed algorithms Ada-CHIO andAda-FBIO,have the low MeanAverage Percentage Error(MAPE)value of error,i.e.,6.8%and 9.5%,respectively.Furthermore,due to the stability of our model our proposed algorithms Ada-CHIO and Ada-FBIO have achieved the accuracy of 93%and 90%.Statistical analysis shows that the hypothesis we proved using statistics is authentic for the proposed technique against benchmark algorithms,which also depicts the superiority of our proposed techniques.

THE DESIGN AND CORRECTION OF A QUANTITATIVE METHOD OF SNOW ESTIMATE BY RADAR

An optimization method is based to design a snowfall estimate method by radar for operational snow warning, and error estimation is analyzed through a case of heavy snow on March 4, 2007. Three modified schemes are developed for errors caused by temperature changes, snowflake terminal velocity, the distance from the radar and calculation methods. Due to the improvements, the correlation coefficient between the estimated snowfall and the observation is 0.66(exceeding the 99% confidence level), the average relative error is reduced to 48.74%, and the method is able to estimate weak snowfall of 0.3 mm/h and heavy snowfall above 5 mm/h. The correlation coefficient is0.82 between the estimated snowfall from the stations 50 to 100 km from the radar and the observation. The improved effect is weak when the influence of the snowflake terminal velocity is considered in those three improvement programs, which may be related to the uniform echo. The radar estimate of snow, which is classified by the distance between the sample and the radar, has the most obvious effect: it can not only increase the degree of similarity, but also reduce the overestimate and the undervaluation of the error caused by the distance between the sample and the radar.The improved algorithm further improves the accuracy of the estimate. The average relative errors are 31% and 27% for the heavy snowfall of 1.6 to 2.5 mm/h and above 2.6 mm/h, respectively, but the radar overestimates the snowfall under1.5 mm/h and underestimates the snowfall above 2.6 mm/h. Radar echo may not be sensitive to the intensity of snowfall, and the consistency shown by the error can be exploited to revise and improve the estimation accuracy of snow forecast in the operational work.

Review:Research Progress for Electric Vehicle Hub Motor Driving Technology

Due to high efficiency,high controllability,high integration,lightweight,and other advantages,electric vehicle with hub motor driving technology has become an emerging trend of chassis technology.This paper concludes the current state⁃of⁃the⁃art of hub motor drive technologies.Firstly,it summarizes recent hub motor drive products and makes suggestions for hub motor drive schemes in different application scenarios.Then research on hub motor drive key technologies such as integrated design,thermal optimization,lightweight,and intensity optimization is investigated.Considering the high response accuracy and zero delay characteristic of hub motor driving system combined with advanced distributed dynamics control technology that can further improve vehicle performance,this paper also analyzes existing chassis dynamics control technologies of hub motor driving system.Considering the development trend of vehicle electrification,intelligentization,network connection,and current research,this paper makes some forecasts for hub motor drive technologies development in the conclusion.

Location of Tibetan earthquakes──a nonlinear approach by a simplex optimized technique

An advanced earthquake location technique presented by Prugger and Gendzwill (1988) was introduced in this paper. Its characteristics are: 1) adopting the difference between the mean value by observed arrival times and the mean value by calculated travel times as the original reference time of event to calculate the traveltime residuals, thus resulting in the 'true' minimum of travel-time residuals; 2) choosing the L1 norm statistic of the residuals that is more suitable to earthquake location; 3) using a simplex optimized algorithm to search for the minimum residual value directly and iteratively, thus it does not require derivative calculations and avoids matrix inversions, it can be used for any velocity structures and different network systems and can solve out hypocentral parameters (λ, ,h) rapidly and exactly; 4) original time is further derived alone, so the trade-off between focal depth and original time is avoided. All these prominent features make us obtain more accurate Tibetan earthquake locations in the rare network condition by using this method. In this paper, we examined these schemes for our mobile and permanent networks in Tibet with artificial data sets,then using these methods, we determined the hypocentral parameters of partial events observed in the field work period of this project from July 1991 to September 1991 and the seven problematic earthquakes during 1989 - 1990. The hypocentral location errors may be estimated to be less than 3. 6 km approximately. The events with focal depth more than 40 km seem to be distributed in parallel to Qinghai-Sichuan-Yunnan arc structural zone.

An Intelligent Approach in Design Optimzation Based on Determining Constraints Compatibility

Based on monotonicity analysis and computer symbolic manipulating technique,a procedure for determining constraints compatibility in design optimization hasbeen proposed in this paper. By using the proposed method relationshipsbetween constrains can be determined and the optimization is greatly simplifid.The method is code with intelligent production systems.

Analytic design of information granulation-based fuzzy radial basis function neural networks with the aid of multiobjective particle swarm optimization

Purpose–The purpose of this paper is to consider the concept of Fuzzy Radial Basis Function Neural Networks with Information Granulation(IG-FRBFNN)and their optimization realized by means of the Multiobjective Particle Swarm Optimization(MOPSO).Design/methodology/approach–In fuzzy modeling,complexity,interpretability(or simplicity)as well as accuracy of the obtained model are essential design criteria.Since the performance of the IG-RBFNN model is directly affected by some parameters,such as the fuzzification coefficient used in the FCM,the number of rules and the orders of the polynomials in the consequent parts of the rules,the authors carry out both structural as well as parametric optimization of the network.A multi-objective Particle Swarm Optimization using Crowding Distance(MOPSO-CD)as well as O/WLS learning-based optimization are exploited to carry out the structural and parametric optimization of the model,respectively,while the optimization is of multiobjective character as it is aimed at the simultaneous minimization of complexity and maximization of accuracy.Findings–The performance of the proposed model is illustrated with the aid of three examples.The proposed optimization method leads to an accurate and highly interpretable fuzzy model.Originality/value–A MOPSO-CD as well as O/WLS learning-based optimization are exploited,respectively,to carry out the structural and parametric optimization of the model.As a result,the proposed methodology is interesting for designing an accurate and highly interpretable fuzzy model.

Control efficiency optimization and Sobol＇s sensitivity indices of MTMDs design parameters for buffeting and flutter vibrations in a cable stayed bridge

This paper studies optimization of three design parameters （mass ratio, frequency ratio and damping ratio） of multiple tuned mass dampers MTMDs that are applied in a cable stayed bridge excited by a strong wind using minimax optimization technique. ABAQUS finite element program is utilized to run numerical simulations with the support of MATLAB codes and Fast Fourier Transform FFT technique. The optimum values of these three parameters are validated with two benchmarks from the literature, first with Wang and coauthors and then with Lin and coauthors. The validation procedure detected a good agreement between the results. Box-Behnken experimental method is dedicated to formulate the surrogate models to represent the control efficiency of the vertical and torsional vibrations. Sobol＇s sensitivity indices are calculated for the design parameters in addition to their interaction orders. The optimization results revealed better performance of the MTMDs in controlling the vertical and the torsional vibrations for higher mode shapes. Furthermore, the calculated rational effects of each design parameter facilitate to increase the control efficiency of the MTMDs in conjunction with the support of the surrogate models.

A NONMONOTONIC TRUST REGION TECHNIQUE FOR NONLINEAR CONSTRAINED OPTIMIZATION

In this paper, a nonmonotonic trust region method for optimization problems with equality constraints is proposed by introducing a nonsmooth merit function and adopting a correction step. It is proved that all accumulation points of the iterates generated by the proposed algorithm are Kuhn-Tucker points and that the algorithm is q-superlinearly convergent.

A Novel Approach for Mitigating Power Quality Issues in a PV Integrated Microgrid System Using an Improved Jelly Fish Algorithm

A two-step methodology was used to address and improve the power quality concerns for the PV-integrated microgrid system. First, partial shading was included to deal with the real-time issues. The Improved Jelly Fish Algorithm integrated Perturb and Obserb (IJFA-PO) has been proposed to track the Global Maximum Power Point (GMPP). Second, the main unit-powered via DC–AC converter is synchronised with the grid. To cope with the wide voltage variation and harmonic mitigation, an auxiliary unit undergoes a novel series compensation technique. Out of various switching approaches, IJFA-based Selective Harmonic Elimination (SHE) in 120° conduction gives the optimal solution. Three switching angles were obtained using IJFA, whose performance was equivalent to that of nine switching angles. Thus, the system is efficient with minimised higher-order harmonics and lower switching losses. The proposed system outperformed in terms of efficiency, metaheuristics, and convergence. The Total Harmonic Distortion (THD) obtained was 1.32%, which is within the IEEE 1547 and IEC tolerable limits. The model was developed in MATLAB/Simulink 2016b and verified with an experimental prototype of grid-synchronised PV capacity of 260 W tested under various loading conditions. The present model is reliable and features a simple controller that provides more convenient and adequate performance.