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In wireless sensor networks (WSN), the multifunctional low-power sensor nodes are linked to base stations and are highly responsible for sensing various information. However, maintaining the network's lifespan is a major issue because of limited battery capacity and node failures. Thus, the proposed study introduced a new Scalable Trust-based Energy Efficient Routing Algorithm (STEERA) with Adaptive Grasshopper Algorithm (AGA) to enhance the network lifespan. At first, the trust values (Tv) are generated for each node to eliminate the malicious node with the help of penalty factor and volatilization. To make efficient routing process, the nodes are formed into cluster groups. A suitable cluster head is selected for each cluster to mitigate the energy consumption problem through the AGA approach by optimizing the parameters like Tv, distance and residual energy. Finally, the data packets are effectively transmitted through a multi-hop routing process by selecting an appropriate path and satisfying certain parameters through Fire Hawks Optimization (FHO). To simulate the proposed techniques, NS2 software is employed, and the performance is measured over various metrics like energy consumption, residual energy, the lifespan of a network, packet loss ratio and average end-to-end delay. The performance analysis shows that the proposed protocol design has obtained higher results than conventional routing protocols. [ABSTRACT FROM AUTHOR]

A new-type identification method in the frequency domain by combining a multi-layer neural network and support vector regression is proposed to identify random load of a complex cylindrical shell structure. The kernel function of support vector regression has a great influence on the prediction accuracy of machine learning model, and it is effective to employ the linear function. As the penalty factor is large, the identification accuracy of the Gaussian kernel function is close to the linear kernel function. In the process of random load identification, the prediction accuracy of the neural network using the L-BFGS method is higher than the traditional Adam method. The number of hidden layers of the neural network has little effect on the L-BFGS algorithm, but a great effect on the Adam method. Different levels of noise are introduced to verify the robustness of the machine learning model. Both the support vector regression with linear kernel function and neural network model based on the L-BFGS method have strong robustness. For the noise percentage of 1%, the support vector regression has better prediction accuracy than the neural network, yet the case is contrary for the noise percentage greater than 5%. The verification shows that the neural network model trained by simulation data has better identification accuracy for real load at some frequencies. The load identification method is proposed based on the frequency points which may establish the machine learning model. The mean absolute percentage error shows that the method based on a multi-layer neural network and support vector regression has high identification accuracy. [ABSTRACT FROM AUTHOR]

Objectives: With the development of modern observation techniques, the processing methods which only consider additive errors cannot meet the requirements. Most of the existing methods for dealing with inequality constraints are based on additive error models, including Gaussian-Markov model and errorsin-variables model, while the processing methods for mixed additive and multiplicative (MAM) random error models are rare. Methods: Based on the least squares principle and the ideas of zero and infinite weights, we construct a penalty function with the given inequality constraints, and derive the simple iterative method (SIM) for the estimation of MAM parameters under the inequality constraints. Then, we add a penalty factor increasing with the number of iterations before the penalty term to address the defects of the original SIM. Results: Two sets of cases show that the improved SIM can effectively solve the problem that the original method does not converge when used to deal with MAM error models with inequality constraints. The structure of improved SIM is simple and easy to implement. And it can obtain better parameter estimation compared with other schemes. Conclusions: The feasibility and effectiveness of the improved SIM for parameter estimation of MAM error models with inequality constraints are verified, and it can be applied to the processing of large batches of data. [ABSTRACT FROM AUTHOR]

Based on fuzzy rough set and whale optimization algorithm, the automatic fault detection method of high-voltage electric energy metering transformer is studied to improve the fault diagnosis effect and efficiency. On the basis of constructing the mathematical model of gradual fault of high-voltage electric energy metering transformer, the fuzzy rough set theory is used to reduce the data attributes of fault samples, eliminate similar attributes, determine the minimum fault feature set, and complete the fault feature selection, which is used as the input of the fault detection model based on Whale Optimization Algorithm-based Support Vector Machine (WOA-SVM). After the kernel parameters and penalty factors of SVM are optimized by whale optimization algorithm, the type of gradual fault of high-voltage electric energy metering transformer is identified. The experimental results show that the reduced fault attributes are distributed differently in the sample data, and the fault detection accuracy can be improved by 9.5 % through fault feature selection. The fault diagnosis model with Gaussian radial basis function, kernel parameter of 0.05 and penalty factor of 10 has the best performance. This method can identify the gradual fault types of high-voltage electric energy metering transformers, and the fault diagnosis effect is outstanding. [ABSTRACT FROM AUTHOR]

Cluster analysis is an important method for data analysis, which is also widely used in remote sensing image classification. An effective clustering algorithm is proposed for the uncertainty caused by the overlap between categories in pattern data and remote sensing images. First, an improved PCM (Possibilistic C-means) algorithm based on distance of cluster center (dc-PCM) is proposed to solve the clustering consistency problem of PCM algorithm. Then, based on dc-PCM algorithm, an improved interval type-2 probabilistic c-means clustering algorithm (IT2-dcPCM) is proposed for interclass maximization. The computational complexity of the proposed algorithm is analyzed, and its performance is evaluated by three data sets, two groups of images segmentation and two groups of remote sensing image classification experiments. The results show that IT2-dcPCM algorithm can assign proper membership degrees to clustering samples, and it has good performance in image segmentation and remote sensing image classification with serious spectral aliasing. [ABSTRACT FROM AUTHOR]

Solar energy offers several environmental, economic, and energy security advantages. Parasitic parameters and shading on solar panels can reduce efficiency. This paper presents a bio-inspired Enhanced Slime Mold (ESM) algorithm search strategy to find near-optimal power points by simulating the behaviour of slime molds in a virtual environment. In a solar panel, proposed ESM provides not only for parameter extraction but also serves as Maximum Power Point Tracking (MPPT) during Partial Shading Conditions (PSC). The proposed ESM dynamic behaviour is examined under solar irradiation and various temperature conditions. The effectiveness of proposed technique has been validated by extracting parameters from conventional polycrystalline and monocrystalline modules in the form of a 5 S–5P arrangement. In the instance of MPPT operation, the proposed ESM algorithm is compared with Ant Bee Colony and Perturb& Observe (ABC-PO) to determine its efficacy. Moreover, during extraction of unknown parameters of solar cell ESM is compared with existing optimization algorithms such as Artificial Bee Swarm Optimization (ABSO), Genetic Algorithm (GA), Covariant Matrix (CM), Ant Bee Colony (ABC), and Advanced Particle Swarm Optimization (APSO). In this connection, proposed ESM algorithm is superior to the above-mentioned algorithms due to high accuracy, a smaller number of computations, and minimum computational time.

In-tube flow condensation plays a vital role in heat exchangers. This study investigates the frictional pressure drop (FPD) and penalty factor (PF) of R123-MWCNTs nanoblend condensing within a horizontal, smooth pipe. MWCNTs, due to their superior thermal conductivity, are appended with base R123 through ultrasonication. The test facility has been designed and fabricated to conduct the proposed experiments with varying parameters and their ranges, such as the mass velocity of about 200 − 500 k g / m 2 s , a refrigerant vapour quality of 0.02 − 0.9 , saturating temperatures of about 35.3 and 40.9 ∘ C , and a volume percentage of MWCNTs of about 0.01 to 0.1 %. The experiments are performed at steady-state conditions. The studies reveal that the nanorefrigerant has a larger FPD than the conventional refrigerant of about 17 − 50 % , 19 − 52 % , and 22 − 53 % at ϕ = 0.01 − 0.1 % , respectively, for mass velocity from 200 − 500 k g / m 2 s , and that this drop increases with MWCNTs concentration. As the saturated temperature rises, the FPD reduces for pure and nano-mixtures of about 6–9%, 7–4%, 7–8%, and 7–5%, respectively, at sustained mass velocity (200 − 500 k g / m 2 s) and the refrigerant vapour quality (0.02 − 0.9). The penalty factor lowers as the refrigerant mass velocity and the vapour quality increase. The accuracy of the present work has been estimated by comparing the experimental FPD with the predicted FPD. The MAPE and MPE are evaluated using the available correlations, and the results show an acceptable agreement of about 12.6, 11.7, 12.7%, and 29% and -5.7%, -5.4%, -5.7%, and -12%, respectively, by Lockhart (1949) , Yan and Lin (1999) , Huang et al. (2010) and Haraguchi et al. (1994) , respectively. [ABSTRACT FROM AUTHOR]

A theoretical study based on the Penalty factor (PF) method by Cavallini et al. is conducted to show that the pressure drop occurring in a wire-on-tube heat exchanger can be converted into a temperature difference for two types of refrigerants R-134a and R-600a typically used for charging refrigerators and freezers. The following conditions are considered: stratified or stratified-wavy flow condensation occurring inside the smooth tube of a wire-on-tube condenser with diameter 3.25, 4.83, and 6.299 mm, condensation temperatures 35°C, 45°C, and 54.4°C and cover refrigerant mass flow rate spanning the interval from 1 to 7 kg/hr. The results show that the PF variation is not linear with vapor quality and attains a maximum when the vapor quality is 0.2 and 0.18 for the R-134a and R-600a refrigerants, respectively. The PF increases with the refrigerant mass flow rate if the inner diameter and saturation temperature constant, and it decreases on increasing the inner diameter to 6.299 mm for constant refrigerant mass flow rate and saturation temperature. The PF for R-600a is higher than that for R-134a due to the lower saturation pressure in the first case. Furthermore, a stratified flow produces higher PF in comparison to the annular flow due to the effect of the surface tension. [ABSTRACT FROM AUTHOR]

Aiming at the difficulty of accurate diagnosis of wheelset-bearing system composite faults, a multi-fault feature extraction method based on self-adaptive variational mode extraction (SAVME) was proposed. Variational mode extraction (VME) can extract a specific sub-signal from a multi-component signal. The key to the success of this algorithm is to determine appropriate initial parameters in advance, including initial center frequency (ICF) and penalty factor. To determine the key parameters of VME adaptively, the convergence characteristics of VME are analyzed deeply, and the VME convergence tendency diagram is proposed creatively according to the trend of the iterative curve of the center frequency of the desired mode. By analyzing the test signal with the VME convergence tendency diagram, the number of main latent sub-signals in the test signal and the ICF of each sub-signal corresponding to the VME can be determined efficiently. Then, according to the position of the ICF of each sub-signal in the frequency domain, the empirical formula of the penalty factor is used to quickly obtain the appropriate penalty factor. The proposed SAVME method not only improves the parameter selection adaptability of the traditional VME algorithm but also extends the VME algorithm to the field of multi-fault diagnosis. By analyzing the simulated signal and two experimental signals, the effectiveness of the SAVME algorithm is verified. Compared with the fast kurtogram method and the adaptive variational mode decomposition method, the proposed method is more accurate and superior in the multi-fault feature extraction of the wheelset-bearing system. [ABSTRACT FROM AUTHOR]

For the constrained mobile robot automatic parking system, the hybrid model predictive control with a penalty factor based on image-based visual servoing (IBVS) is proposed to address the problem of feature point loss and emergency braking in dynamic obstacle scenarios caused by excessive target bias gain when using traditional IBVS control methods. The traditional IBVS control is transformed into an optimization problem with constraints in the finite time domain, by defining the optimization function based on the mobile robot's positional deviation and image feature point deviation, while using actuator saturation and speed limit as constraints. Based on this, a convex optimization function with penalty factors is defined and combined with incremental model predictive control. This control strategy could ensure the emergency braking performance of the mobile robot when the image feature points are massively obscured by obstacles in dynamic scenes, while improving the accuracy and real-time of its trajectory tracking control. Finally, simulation comparisons are conducted to verify the effectiveness of the proposed control method. [ABSTRACT FROM AUTHOR]

• Revealed the mechanism and influence of the shape factor on DRT deconvolution; • Evaluated the optimisation methods of the shape factor by the synthetic EIS data; • Discussed the coupling effect of the shape and penalty factors on DRT deconvolution; • Proposed a collaborative optimisation strategy to improve the accuracy of DRT result; Electrochemical impedance spectroscopy (EIS) is a powerful diagnosis tool for the performance of electrochemical energy storage and conversion devices. One challenge for EIS-based diagnosis is the difficulty in separating highly overlapped impedance spectra. To overcoming this challenge, the distribution of relaxation times (DRT) method based on Tikhonov regularization algorithm can be employed. However, the accuracy and stability of the DRT deconvolution method not only depend on the penalty factor (the actual regularization tool), but also on the rarely studied shape factor (a potential regularization tool). In this study, a comprehensive investigation on the effect of the shape factor on both the accuracy and the stability of the DRT deconvolution method was conducted. First, the influence of the shape factor on the DRT deconvolution method was theoretically derived and numerically simulated. Second, the optimization methods for the shape factor and the selection of regularization matrices for DRT deconvolution were quantitatively evaluated using synthetic impedance data. Third, the coupling effect of the shape and penalty factors on the DRT deconvolution method was analyzed quantitatively. Finally, the collaborative optimization strategy was proposed and evaluated. The method presented in this paper can be used to improve the accuracy and reliability of the DRT to decode highly overlapped impedance spectra. [ABSTRACT FROM AUTHOR]

This paper uses support vector regression (SVR) to predict short-term traffic flow, and studies the feasibility of SVR in short-term traffic flow prediction. The short-time traffic flow has many influencing factors, which are characterized by nonlinearity, randomness and periodicity. Therefore, SVR algorithm has advantages in dealing with such problems. In order to improve the prediction accuracy of the SVR, this paper uses genetic algorithm (GA) to optimize the SVR and other parameters to obtain the global optimal solution. The optimal parameters are used to construct the SVR prediction model. This paper selects the traffic flow data of the Jiangxi Provincial Transportation Department database to verify the feasibility and effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Central South University is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper proposes an efficient technique for optimum scheduling of micro-grids with multi-period islanding restrictions. The proposed method is joint implementation of Cuttle Fish Algorithm (CFA) and Crow Search Algorithm (CSA); is known CFCS method. Here, the CFA search behavior is modified by the CSA update position. The proposed CFCS system is utilized for optimal scheduling of micro-grid and considerably decreases the calculation load. The main purpose of proposed work is diminish the micro-grid operation cost together with the dispatch-able units operation cost, cost of power transmission as main grid with inconvenience cost recognized with consumers. The cost of power transmission from main phase can be positive or negative based on the flow direction of transmission line linking the microcircuit to main phase. Negative cost that represents an export of power to main grid emerges as economic advantage of micro-grid. Cost of inconvenience implies the penalty of modifiable loads programming outside the time intervals particular by the consumers. Constant penalty factor is utilized for prioritizing loads with respect to operation sensitivity inside the particular time intervals, in which a superior value of the penalty factor implies a least flexible load based on time interval operation settings. The value of the penalty factor is chosen reasonably greater to unit generation cost and market price. In proposed technique, the CFCS is used for establishing the correct program of the MG combinations according to load side power range. The CFCS method, objective function can be defined as system data subject to equality and inequality limitations. At that time, CFCS model is performed at MATLAB/Simulink working platform and performance is estimated to existing techniques. [ABSTRACT FROM AUTHOR]

This paper proposes a simplified elastic-plastic analysis procedure using the penalty factors presented in the Code Case N-779 for strain-based fatigue assessment of nuclear safety class 1 components under severe seismic loads such as safety shutdown earthquake and beyond design-basis earthquake. First, a simplified elastic-plastic analysis procedure for strain-based fatigue assessment of nuclear safety class 1 components under the severe seismic loads was proposed based on the analysis result for the simplified elastic-plastic analysis procedure in the Code Case N-779 and the stress categories corresponding to normal operation and seismic loads. Second, total strain amplitude was calculated directly by performing finite element cyclic elastic-plastic seismic analysis for a hot leg nozzle in pressurizer surge line subject to combined loading including deadweight, pressure, seismic inertia load, and seismic anchor motion, as well as was derived indirectly by applying the proposed analysis procedure to the finite element elastic stress analysis result for each load. Third, strain-based fatigue assessment was implemented by applying the strain-based fatigue acceptance criteria in the ASME B&PV Code, Sec. III, Subsec. NB, Article NB-3200 and by using the total strain amplitude values calculated. Last, the total strain amplitude and the fatigue assessment result corresponding to the simplified elastic-plastic analysis were compared with those using the finite element elastic-plastic seismic analysis results. As a result of the comparison, it was identified that the proposed analysis procedure can derive reasonable and conservative results.

Copyright of Journal of Jilin University (Science Edition) / Jilin Daxue Xuebao (Lixue Ban) is the property of Zhongguo Xue shu qi Kan (Guang Pan Ban) Dian zi Za zhi She and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

We know that the exact solutions for most of stochastic age-structured capital systems are difficult to find. The numerical approximation method becomes an important tool to study properties for stochastic age-structured capital models. In the paper, we study the numerical solution for stochastic age-dependent capital system based on explicit-implicit algorithm and discuss the convergence of numerical solution. For the practical significance of capital, we need to consider the positivity of the numerical solution. Therefore, we introduce a penalty factor in the stochastic age-dependent capital system to maintain the positivity, and analyze the convergence of the positive numerical solution. Finally, an example is given to verify our theoretical results. [ABSTRACT FROM AUTHOR]

Despite the inherent stability of Quadratic Approximation based Jaya (JaQA) in solving constrained optimization problems, its effectiveness in exploring the search space is limited. Furthermore, the user-defined penalty parameter utilized to mitigate constraint violation poses another impediment to the efficiency of JaQA. In light of these drawbacks, this paper introduces a new meta-heuristic algorithm, namely Self-adaptive Multi-population Quadratic Approximation guided Jaya (SMP-JaQA). The three additional key features of the SMP-JaQA over JaQA are (i) the inclusion of a multi-population technique to ensure sufficient diversity in the search process (to address the exploration), (ii) a self-adaptation strategy to make it more user-friendly for complex problems in various domains (to address the exploration further), and (iii) a dynamic penalty factor (to automate the penalty parameter). These novel changes help strike a balance between exploration and exploitation too by identifying potential areas in the search space and reduces the likelihood of getting stuck in a local optima. The validity of SMP-JaQA is tested over 18 and 28 benchmark functions of varying dimensions from the CEC-2010 and CEC-2017 test suites, respectively. In order to justify the wide applicability of the proposed algorithm, a set of four real-life constrained problems (includes two mechanical design problems, an electronics design problem and an agricultural problem) are being solved through SMP-JaQA. A thorough analysis of computational outcomes, exploration–exploitation phases, and scalability are presented to shed light on the efficacy and stability levels of SMP-JaQA. The results are compared with those of other state-of-the-art meta-heuristics using Friedman's and Wilcoxon Signed-Rank tests. The outcomes indicate that SMP-JaQA produces reasonable solutions in terms of global optimality and solution quality. [ABSTRACT FROM AUTHOR]

In this research, multi-objective optimization of VPP is performed by considering multiple renewables and co-generation sources and solved by using the recently developed multi-objective Harris Hawk's optimization (MOHHO) algorithm. Renewable sources comprise solar Photovoltaic (PV), wind power, fuel cells along with energy storage systems (ESS), electric vehicles (EV), along with CHP units are associated to form a VPP system. The penalty factor approach is implemented along with the weighting factor to develop a multi-objective framework that can simultaneously capitalize on the net profit and minimalize the emission while taking proper care of the related constraints. In this research, EVs are considered as a flexible reserve option along with a dedicated ESS applicable as a spinning reserve. Also, a dynamic pricing-based strategy is also presented and the optimization of VPP is accomplished by implementing a multi-objective-based day-ahead scheduling (MODAS) to see the behaviour of the anticipated system with a suitable method of constraint handling. Four diverse illustrations are considered in three different case study which is performed for hourly-based scheduling and the results are compared with the proposed technique. Statistical analysis is performed and the quality solution sets are attained by the HHO algorithm after performing 100 independent trials and the same is correlated with the available studies that indicate the efficacy and appropriateness of the selected approach. [Display omitted] • This work evaluated a VPP system comprise of multiple RERs. • Multiple reserve provisions are equipped to the developed VPP as EV and ESS. • Peak valley based dynamic pricing scheme is applied to increase profit margin. • HHO is employed to solve the anticipated VPP problem. • Performance of VPP is validated through multiple illustrations and implication are emphasized from the contextof SDG. [ABSTRACT FROM AUTHOR]

Support vector machine (SVM) is regarded as one of the most effective techniques for supervised learning, while the Gaussian kernel SVM is widely utilized due to its excellent performance capabilities. To ensure high performance of models, hyperparameters, i.e. kernel width and penalty factor must be determined appropriately. This paper studies the influence of hyperparameters on the Gaussian kernel SVM when such hyperparameters attain an extreme value (0 or ∞). In order to improve computing efficiency, a parameter optimization method based on the local density and accuracy of Leave-One-Out (LOO) method are proposed. Kernel width of each sample is determined based on the local density needed to ensure a higher separability in feature space while the penalty parameter is determined by an improved grid search using the LOO method. A comparison with grid method is conducted to verify validity of the proposed method. The classification accuracy of five real-life datasets from UCI database are 0.9733, 0.9933, 0.7270, 0.6101 and 0.8867, which is slightly superior to the grid method. The results also demonstrate that this proposed method is computationally cheaper by 1 order of magnitude when compared to the grid method. [ABSTRACT FROM AUTHOR]

In the absence of demand-side bids for certain reliability products in the wholesale electricity markets, Independent System Operators (ISOs) traditionally use fixed demand requirements with penalty factors to clear the market. This approach does not allow proper tradeoffs between cost and reliability due to the inelasticity of the fixed requirements. Therefore, ISOs have been replacing the fixed requirements with sloped demand curves. However, the economic interpretation of demand curves, especially for multiple coupled products, is largely missing in both theory and practice. This could lead to the misrepresentation of the demand benefit in the market clearing objective. Using a two-product market model, Part I of this paper reveals two distinct interpretations of demand curves, each associated with a specific form of the market clearing problem. This implies that the construction of demand curves and the use of them in market clearing must be consistent with their economic interpretation. In Part II of this paper, we analyze the sequential and iterative market clearing processes currently implemented in some markets to demonstrate their solution sub-optimality. The root cause of the problem is the lack of a clear representation of the demand benefit. The social surplus optimization based on the proper interpretation of demand curves is proposed. [ABSTRACT FROM AUTHOR]

Following Part I of this paper that reveals two distinct interpretations of demand curves and the corresponding different forms of the market clearing formulation, Part II of this paper analyzes the sequential and the iterative market clearing algorithms currently implemented in some markets. It is shown that these algorithms lead to socially sub-optimal solutions. The root cause of the problem is the lack of a clear representation of the demand benefit. The social surplus optimization based on the proper interpretation of demand curves is then proposed. A small numerical example is used to demonstrate the significant optimality gap between the socially optimal solution and the sequential/iterative solution. The potentially large impacts of the gap on the societal benefit and the individual resource revenues entail a close examination of these algorithms. [ABSTRACT FROM AUTHOR]

Mobile Ad Hoc Network (MANET) has a set of mobile nodes that are allowed to communicate with each other through wireless links. The nodes are deployed spontaneously without any infrastructure in a geographical area. Due to the lack of centralized administration and prior organization, MANETs are vulnerable to different attacks of malicious nodes. To overcome the problem of black hole attack in MANETs, a trust model using Differential Evolution (DE) algorithm has been proposed. It identifies the malicious node and inhibits them to become the member of data transmission path. The proposed work consists of two phases; one is to obtain the optimized path and the other deals with the penalty factor for malicious nodes. Moreover, the Differential Evolution is one of the most promising optimization to enhance security with increased network density. The proposed algorithm is compared with Ad Hoc on Demand Multipath Distance Vector (AOMDV), Dynamic Source Routing (DSR), Genetic algorithm and Ant Colony Optimization (ACO). [ABSTRACT FROM AUTHOR]

Electric vehicles (EV) offer a sustainable solution to the emission problems from transportation but require appropriate charging management facilities. This article presents a decentralized charging management scheme for EV scheduling in a multiaggregator scenario and is compared with a centralized scheduling (CES) strategy in terms of performance. Mobility-aware scheduling is implemented, where the EV arrival at charging stations is modeled using a survey conducted for the city of Jaipur, India, to evaluate the travel patterns. A new approach for modeling the willingness of the consumer has been presented that evaluates the willingness based on scheduling parameters, which include the charging cost, the charging duration, and the arrival/scheduling time of the vehicle. A penalty function has also been developed for the inclusion of the customer willingness into the profit equation. Results indicate superior performance of decentralized scheduling (DES) as compared with CES. Furthermore, the developed willingness indicator accommodates the preference of the EV driver, and the penalty factor represents the EV user's agreement with the scheduling in the profits. DES in collaborative scheduling provided the highest profits for the aggregators. [ABSTRACT FROM AUTHOR]

This paper aims to percolate energy management of microgrid systems by minimizing the generation cost of the same. Energy management of microgrid refers to the optimal sizing and scheduling of the distributed energy resources to reduce the generation cost and pollutant emission. A recently developed crow search algorithm (CSA) is implemented to execute the optimization. The proposed CSA imitates the crows' memory and tactics of hiding and chasing their food. Six renewable integrated microgrid test systems and a total of eighteen different cases are considered for this study. Various practical complexities such as valve point loading effect, combined economic–emission dispatch using price penalty factor method, modeling of the renewable energy sources and energy storage systems are taken into consideration for energy management of the microgrid systems. Results obtained are then compared to a number of different soft computing techniques such as genetic algorithm and particle swarm optimization and the likes to justify the effectiveness of the proposed algorithm. A statistical analysis, viz. Wilcoxon signed-rank test, is performed to prove the superiority of the proposed approach over the various other optimization techniques used in the paper. [ABSTRACT FROM AUTHOR]

Constraint relaxation is widely used in electricity market clearing to obtain an acceptable solution when the market model is infeasible facing conflicting operating requirements. However, the penalty factors added in the objective function cause extremely high price spikes. Such a problem is currently handled by ad-hoc manners in power industries. After summarizing the current industrial practice and relative problems, we present a unified pricing model under constraint violations, which considers the prices as decision variables. Theoretical proof shows that the proposed method is capable of representing LMP with price spikes and current industrial practice. Hence, the pricing model has the ability of achieving more desired prices under the same pricing requirements. Furthermore, this paper presents a practical implementation of this unified model. The minimization of uplift payments, which is caused by the split of pricing and scheduling, is used as the objective function. This model can be transformed to a linear formulation without accuracy loss. Case studies show that the proposed pricing method brings distinct advantages compared with current industrial practices. [ABSTRACT FROM AUTHOR]

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper proposes penalty factor equations that take into consideration the weld strength over-match given in the classified form similar to the revised equations presented in the Code Case N-779 via cyclic elastic-plastic finite element analysis. It was found that the Ke analysis data reflecting elastic follow-up can be consolidated by normalizing the primary-plus-secondary stress intensity ranges excluding the nonlinear thermal stress intensity component, Sn to over-match degree of yield strength, MF. For the effect of over-match on Kn × Κν, dispersion of the Kn × Κν analysis data can be sharply reduced by dividing total stress intensity range, excluding local thermal stresses, Sp-lt by MF. Finally, the proposed equations were applied to the weld between the safe end and the piping of a pressurizer surge nozzle in pressurized water reactors in order to calculate a cumulative usage factor. The cumulative usage factor was then compared with those derived by the previous Ke factor equations. The result shows that application of the proposed equations can significantly reduce conservatism of fatigue assessment using the previous Ke factor equations.

Copyright of Journal of Guangdong University of Technology is the property of Journal of Guangdong University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract -- In electricity markets, transmission constraint relaxation is frequently used to ensure the feasibility of the optimal power flow (OPF) problem and avoid the expensive yet ineffective congestion adjustment. However, transmission constraint penalty factors have a considerable impact on the market scheduling and prices. There is still no consensus on how to properly set the penalty factors. In this paper, the impact of transmission constraint penalty factors on the market solution is quantitatively investigated via the sensitivity analysis of the DC OPF model. Parametric programming is performed to determine the change of the market scheduling and prices with respect to the change of penalty factors. In particular, we prove for the very first time that when the objective function of the market model is quadratic, the parametric programming can be conveniently performed without the need of re-optimizing the DC OPF model in most cases. On this basis, an efficient method is proposed for exactly characterizing the mapping between the penalty factors and the market solution. Case studies show that the proposed method could be up to 100 times quicker than re-optimizing the DC OPF model as in the traditional parametric programming process. Highlights • Influence of penalty factors on DC OPF solution is quantitatively studied. • Parametric programming for the market model is performed without re-optimization. • Proposed method for calculating the mapping is accelerated by more than 100 times. [ABSTRACT FROM AUTHOR]

Owing to the growing interest in environmental problems worldwide, it is essential to schedule power generation considering the effects of pollutants. To address this, we propose an optimal approach that solves the combined economic emission dispatch (CEED) with maximum emission constraints by considering demand response (DR) program. The CEED consists of the sum of operation costs for each generator and the pollutant emissions. An environment-based demand response (EBDR) program is used to implement pollutant emission reduction and facilitate economic improvement. Through the weighting update artificial bee colony (WU-ABC) algorithm, the penalty factor that determines the weighting of the two objective functions is adjusted, and an optimal operation solution for a microgrid (MG) is then determined to resolve the CEED problem. The effectiveness and applicability of the proposed approach are demonstrated via comparative analyses at a modified grid-connected MG test system. The results confirm that the proposed approach not only satisfies emission constraints but also ensures an economically superior performance compared to other approaches. These results present a useful solution for microgrid operators considered environment issues.

Initial calibration is a great challenge for cuff-less blood pressure (BP) measurement. The traditional one point-to-point (oPTP) calibration procedure only uses one sample/point to obtain unknown parameters of a specific model in a calm state. In fact, parameters such as pulse transit time (PTT) and BP still have slight fluctuations at rest for each subject. The conventional oPTP method had a strong sensitivity in the selection of initial value. Yet, the initial sensitivity of calibration has not been reported and investigated in cuff-less BP motoring. In this study, a mean point-to-point (mPTP) paring calibration method through averaging and balancing calm or peaceful states was proposed for the first time. Thus, based on mPTP, a factor point-to-point (fPTP) paring calibration method through introducing the penalty factor was further proposed to improve and optimize the performance of BP estimation. Using the oPTP, mPTP, and fPTP methods, a total of more than 100,000 heartbeat samples from 21 healthy subjects were tested and validated in the PTT-based BP monitoring technologies. The results showed that the mPTP and fPTP methods significantly improved the performance of estimating BP compared to the conventional oPTP method. Moreover, the mPTP and fPTP methods could be widely popularized and applied, especially the fPTP method, on estimating cuff-less diastolic blood pressure (DBP). To this extent, the fPTP method weakens the initial calibration sensitivity of cuff-less BP estimation and fills in the ambiguity for individualized calibration procedure.

Copyright of Acta Automatica Sinica is the property of Chinese Academy of Sciences, Institute of Automation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)