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Wind is an essential, clean and sustainable renewable source of energy; however, wind speed is stochastic and intermittent. Accurate wind power generation forecasts are required to ensure that power generation can be scheduled economically and securely. This work proposes a hybrid deep learning technique that incorporates a quantum-inspired neural network to predict wind speeds 24 h in advance. An innovative neural network technique is implemented by cascading parallel convolutional neural networks (CNNs) with a long short-term memory (LSTM) and a quantum-inspired neural network (QINN). The proposed hybrid model is optimized using two iterative loops: the outer loop is implemented by using quantum particle swarm optimization (QPSO) to tune the structure of model and other hyperparameters/parameters. The inner loop uses an Adam optimizer to tune the weights and biases of the proposed model. Spatiotemporal wind speeds at various locations provide the 2D input data. Simulation results reveal that the proposed method outperforms other methods for 24 h-ahead wind speed forecasting. [ABSTRACT FROM AUTHOR]

Some researchers consider evaluative processes as a foundation of social perception. Different approaches of social knowing that were linked to theories on the nature of personality include trait diagnosis and evaluative encoding of social information and focusing on understanding emotions, goals and beliefs. These methods explain how people understand the social world consistent with the cognitive processes they engage in.

• The convolution and pooling operations are used to extract the wind power feature. • A double Gaussian activation function is proposed for Radial Basis Function network. • Coefficients of determination (r-square) of the results exceed 0.88 in 4 seasons. Wind power generation is always associated with uncertainties as a result of fluctuations of wind speed. Accurate predictions of wind power generation are important for the efficient operation of power systems. This paper presents a hybrid deep learning neural network for 24 h-ahead wind power generation forecasting. This novel method is based on a Convolutional Neural Network (CNN) that is cascaded with a Radial Basis Function Neural Network (RBFNN) with a double Gaussian function (DGF) as its activation function. The CNN is utilized to extract wind power characteristics by convolution, kernel and pooling operations. The supervised RBFNN, incorporating a DGF, deals with uncertain characteristics. Realistic wind power generations, measured on a wind farm, were used in simulations. The proposed method is implemented using TensorFlow and Keras Library. Comparative studies of different approaches are shown. Simulation results reveal that the proposed method is more accurate than traditional methods for 24 h-ahead wind power forecasting. [ABSTRACT FROM AUTHOR]

Short-term electric load forecasting is essential for the operation of power systems and the power market, including economic dispatch, unit commitment, peak load shaving, load management and bidding strategy development. This paper presents a novel method that uses a hybrid convolutional neural network (CNN) that is cascaded with a fully-connected network, to conduct 24h-ahead electric load forecasting in power systems. Spearman's rank-order correlation, which serves as input size information for the CNN, was used to measure the cross-correlation between two sets of observations (chronological loads) of time shifts or lags. Both max and average pooling operations were utilized to extract data features in concatenation while spatial and conventional dropouts were employed to avoid overfitting in the hybrid CNN. The proposed method used two loops to yield the optimal CNN. The outer loop optimizes the structure and hyperparameters (such as kernel size) of the hybrid CNN by a proposed method of enhanced elite-based particle swarm optimization (EEPSO) while the inner loop optimizes the parameters (such as values of a kernel) and weights in neural networks using the Adam optimizer. The EEPSO is based on the mean search and chaotic descending inertia weight. The proposed method was used to study the 2018 and 2019 hourly load data in Taiwan Power Company. Simulation results show that the proposed method outperforms the well-known ARIMA, Radial Basis Function Network, Support Vector Regression, Long Short-term Memory and their hybrid variants. • Structure and hyperparameters of the CNN are optimized by enhanced elite-based PSO. • Both spatial and traditional dropouts are used to avoid overfitting in the CNN model. • Both max and average pooling operations are used to extract data features. • Hourly load data in Taiwan Power Company in 2018 and 2019 are studied. [ABSTRACT FROM AUTHOR]

Solar photovoltaic (PV) cells have attracted substantial interest recently owing to environmental concerns and the energy crisis. However, the efficiency of power generation by solar PV cells depends on various factors, such as climatic conditions and tilt angle. Therefore, maximum power point tracking (MPPT) for PV modules is important. This work proposes a novel robust design, which relies on various combinations of insolations, temperatures, and tilt angles, in a stand-alone PV system with MPPT. Orthogonal experiments are carried out using the Taguchi method, avoiding a full factorial experimental design. In the proposed Taguchi-based MPPT robust design of the PV system, insolation and temperature are regarded as an input and ‘noise’, respectively; insolation passage, tilt angle, and load resistance are factors that affect the results of the Taguchi experiments in multiple scenarios. A fuzzy logic controller, tuned by chaos particle swarm optimization (PSO) was utilized to implement MPPT. A stand-alone PV system was used to demonstrate the results that were obtained by the robust design of MPPT and applicability of the proposed method. Finally, a Digital Signal Processor-in-the-Loop simulation was performed using an OP5600 real-time digital simulator by Opal-RT to demonstrate the viability of the proposed scheme. [ABSTRACT FROM AUTHOR]

The installation of renewable energy generation resources (such as photovoltaic arrays and wind-turbine generators) in a microgrid is important because a microgrid can increase the penetration of renewable energies in a smart grid. A community may be a grid-tied microgrid in which an energy management system may dispatch elastic loads (such as air conditioning systems). This paper investigates the optimal sizing of renewable energy generation resources in a community microgrid. The cost of renewables and community welfare are optimized while the comfort zone of indoor temperature in all homes is maintained using air conditioning systems. Community welfare is ensured by minimizing the purchased power from and maximizing the sold power to the utility grid with different time-of-use electricity tariffs. Since the problem of interest involves a large number of variables and chronological constraints, Markov models of photovoltaic power generation, wind generation, load and temperature are utilized to reduce the numbers of variables and constraints. The Markov-based optimization problem is then solved using the interior-point algorithm. The simulation results, based on a smart community of 50 homes, reveal the applicability of the proposed method. [ABSTRACT FROM AUTHOR]

Operations in an electric distribution system become challenging because the penetration of renewables (such as large photovoltaic (PV) arrays) becomes high nowadays. The detection and classification of faults in PV arrays are crucial for energy management and the mitigation of financial losses. The convolutional neural network (CNN) is one of the most popular deep learning-based methods for solving detection and classification problems. The use of CNNs in various fields has yielded promising results owing to their ability to extract features of signals. This study presents a 3D CNN for PV fault detection and classification. Both direct current (DC) and alternating current (AC) signals in the PV system are converted to 3D images by using the Gramian Angular Field (GAF) transform for signal pre-processing. The proposed method yields promising results in terms of overall accuracy (OA) of testing data. Simulation results indicate that the proposed 3D CNN outperforms other machine learning (ML) methods, such as k-nearest neighbor, Random Forest, Decision Tree, and Support Vector Machine, when applied to the problem of interest. • 3D CNN is used to detect and classify faults in the PV system. • Each signal in the PV array is transformed to a 2D image by GAF. • Both AC and DC signals are utilized to improve accuracy. [ABSTRACT FROM AUTHOR]

Abstract: A stand-alone power system is an independent system that comprises micro-turbines, diesel generators, a wind park, solar photovoltaic (PV) modules and/or energy storages, etc. This paper presents a new method for dealing with the short-term active power scheduling of a stand-alone system. The fuel cost of diesel units and CO2 emission are minimized and all operation constraints are satisfied. The maximum wind and solar PV powers with uncertainties are modeled using fuzzy sets. Adaptive chaos clonal evolutionary programming (ACCEP) is employed to solve this problem, which is formulated as an interactive multi-objective problem. Different degrees of fuzziness, preferred reference levels, and priority list for diesel generators are discussed. Simulation results show the applicability of the proposed method. [Copyright &y& Elsevier]

Abstract: Power quality in distribution systems may deteriorate due to increased time-varying and nonlinear loads. The harmonic current is one of the probabilistic and nonlinear pollution sources causing voltage distortion. The harmonic currents and bus loads in a power system are non-deterministic in nature. The single-tuned passive filter is considered an economical but efficient approach in reducing harmonics. This paper presents a new method for studying passive filter planning using cumulants and the Adaptive Dynamic Clone Selection Algorithm (ADCSA). The harmonic voltages, calculated by cumulants, correspond to those whose values of cumulative probabilities are 0.95. ADCSA was employed because of its ability to search the global optimum. The number (integer) of capacitor bank and tuned resonance frequency (real number) of the single-tuned passive filter are considered unknowns. The simulation results from an 18-bus system with multiple time-varying loads and harmonic currents show the applicability of the proposed method. [Copyright &y& Elsevier]

Abstract: As the business world becomes more global many managers have spent significant time studying and working abroad. Does this overseas experience re-shape how managers think about the world? In this study we examined attribution patterns of Taiwanese managers who have studied and worked abroad. We found that managers who have been abroad switch their cultural orientation as a result of being shown Western or Chinese cultural icons, but this effect only occurs for those high in bicultural identity integration (BII). We confirmed that this effect occurs when “environmental” priming is used, and also confirmed that this effect is found when examining pay allocation decisions (a typical managerial issue) in addition to attribution patterns. These results point to the benefits of hiring internationally experienced managers, but also suggest that international experience may not be enough—companies need to also assess managers’ BII to know if foreign experience will truly translate into culturally appropriate cognitive flexibility. [Copyright &y& Elsevier]

Abstract: Despite deliberate efforts to promote the ideal of “One world, One dream,” the 2008 Beijing Olympics appears to have exaggerated Mainland Chinese’ perception of Chinese and Western cultural differences and increased low ingroup identifiers’ ingroup favoring emotions and perceptions. In Study 1, we measured Beijing Chinese''s perceptions of Chinese and Western values before and after the Olympics. The results showed that, after the Olympics, encountering the Beijing Olympic icon increased perceived value differences between Western and Chinese cultures. Study 2 showed that in Mainland China, individuals who identified strongly with Chinese culture favored Chinese (vs. American) commercial brands more both at the beginning and toward the end of the Olympics. Moreover, although individuals with low levels of Chinese cultural identification did not display significant ingroup favoritism at the beginning of the Olympics, they did so toward the end of the Games. Together, the results suggest that the Olympics had widened the cultural divide between China and the Western world. [Copyright &y& Elsevier]

Abstract: Wind energy is currently one of the types of renewable energy with a large generation capacity. However, since the operation of wind power generation is challenging due to its intermittent characteristics, forecasting wind power generation efficiently is essential for economic operation. This paper proposes a new method of wind power and speed forecasting using a multi-layer feed-forward neural network (MFNN) to develop forecasting in time-scales that can vary from a few minutes to an hour. Inputs for the MFNN are modeled by fuzzy numbers because the measurement facilities provide maximum, average and minimum values. Then simultaneous perturbation stochastic approximation (SPSA) algorithm is employed to train the MFNN. Real wind power generation and wind speed data measured at a wind farm are used for simulation. Comparative studies between the proposed method and traditional methods are shown. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a method that integrates deterministic approach with fault-tree analysis for reliability assessment of a composite system (generation and transmission in power systems). The contingency screening is conducted in the first step. The results are further classified into three clusters in the second step: normal, local trouble and system trouble. The fault-tree analysis is used to assess the reliability of the composite system in the third step. Finally, Risk Reduction Worth is adopted as a measure of importance for identifying the crucial element that has significant impact on the reliability. In this paper, a composite system in Taiwan serves as an example for illustrating the simulation results attained by the proposed method. The simulation results, verified by Siemens PTI PSS/E TPLAN software package, show that the proposed method is applicable for large scale power systems. [Copyright &y& Elsevier]

This paper applies new maximum-power-point tracking (MPPT) algorithms to a wind-turbine generator system (WTGS). In this paper, the WTGS is a direct-drive system and includes the wind-turbine, permanent-magnet (PM) synchronous generator, three-phase full bridge rectifier, buck-boost converter and load. The new MPPT method uses gradient approximation (GA) algorithm. Three methods based on GA for achieving MPPT are discussed in this paper: (1) full-sensor control with anemometer and tachometer, (2) rule-based method and (3) adaptive duty cycle method. The third method has merits of no PID parameters, proportional constant, anemometer, tachometer and characteristics of WTGS required. This method enables the permanent-magnet synchronous generator (PMSG) to operate at variable speeds to achieve good performance. Simulation results show that the tip-speed ratio (TSR) and power coefficient obtained by the adaptive duty cycle method with GA can be almost identical to the optimal values. [Copyright &y& Elsevier]

Abstract: Voltage sag, caused by faults, is an important power quality problem. Static transfer switches (STSes) are considered to be one of the efficient remedies for voltage sags. Determination of the installation locations for STSes in a distribution system, therefore, becomes essential for planning a system with sensitive loads. In this paper, the problem is formulated as a fuzzy multi-objective programming problem considering STS investment cost, sensitive bus voltage deviations and total voltage sags. Genetic Algorithms (GAs) using a vertex encoding/decoding are used to solve the problem. The vertex-based Prufer number is used in GAs for encoding/decoding the chromosomes (strings). The Prufer number ensures that the distribution system structure is radial in the iterations. The simulation results obtained from a 33-bus system are used to show the applicability of the proposed method. [Copyright &y& Elsevier]

Abstract: Bidding competition is a main transaction approach in a deregulated market. Locational marginal prices (LMPs) resulting from bidding competition signal electricity values at a node or in an area. The LMP reveals important information for market participants to develop their bidding strategies. Moreover, LMP is also a vital indicator for the Security Coordinator to perform market redispatch for congestion management. This paper presents a method using fuzzy reasoning and recurrent neural networks (RNNs) for forecasting LMPs. The fuzzy rules are used to perform the linguistic reasoning about the contingencies. The reasoning results serve as a part of inputs to the RNNs for forecasting the LMPs. The historical LMPs in the PJM market are used to test the proposed method. It is found that the proposed neuro-fuzzy method is capable of forecasting LMP values efficiently. [Copyright &y& Elsevier]

• Daily load curve becomes "duck–curve" due to high penetration of renewables. • Significant mitigation in greenhouse gas emission in winter of 2025. • Pumped storage units become crucial energy storages in daytime due to solar power. • Combined-cycle units play a critical role to meet spinning reserve requirements. This study presents the effect of Taiwan's energy policy on its unit commitment in the year 2025. Taiwan's energy policy has been changing since 2005, since when it has required the decommissioning of nuclear power plants in response to the crisis in Fukushima, the adoption of the Kyoto Protocol, and the increase of renewable sources of power generation to 20% of its energy mix. The paper models the unit commitment for the Taiwan power system under Taiwan's energy policy. The study compares unit commitments in 2018, summer 2025, and winter 2025 using AMPL Software with the CPLEX 12.9.0 Solver. The results show that the net demand curve will change by 2025, forming a "duck – curve" model due to the high penetration of renewable energy. The daily carbon dioxide emission in 2018 for five percent (5%) spinning reserve is 392,151,000 kg. The said amount is larger than in winter 2025 when it will be 311,314,000 kg indicating the importance of the full implementation of renewable energy projects in helping to reach Taiwan's goal to limit carbon dioxide emission by 2025. Pumped storage scheduling changes from conventional pumping in the early morning to pumping from around noon to late afternoon due to the high penetration of renewable energy. The construction of combined-cycle units will have a critical role in the achieving of the spinning reserve capacity goal set by the Taiwan government, to avoid the importation of energy and to maintain the energy security of the country. [ABSTRACT FROM AUTHOR]

The operations of power systems are becoming increasingly challenging due to the high penetration of renewable power generation, which is uncertain and stochastic. Highly accurate day-ahead renewable power forecasting helps operators in a dispatch center schedule power generations for conventional thermal generators. Efficient day-ahead renewable power forecasting requires information from widely separated locations. This paper proposes a novel day-ahead spatiotemporal wind speed forecasting method based on a convolutional neural network (CNN), which is an image-based deep learning method. This paper exploits a robust design based on Taguchi's orthogonal array to determine the numbers of cascade/parallel layers, convolutions in each layer, kernels and hidden layers, as well as the kernel size, zero-padding and dropout ratio in the proposed CNN to ensure that the proposed CNN is insensitive to the seasonal characteristics of wind speeds while retaining high accuracy. In this paper, the day-ahead forecasting of wind speed at an offshore wind farm (Fuhai) near Taiwan is performed using historical wind speeds at seven sites in Taiwan, China, South Korea and the Philippines. Simulation results reveal that the proposed robust design-based CNN outperforms existing methods. Image 1 • Structure parameters of the CNN are determined by robust design. • Hyperparameters of the CNN are determined by robust design without empirical tests. • Spatiotemporal wind speeds provide the 2D information applied to the CNN. • Other ancillary wind farms are determined by cross-correlation analysis. [ABSTRACT FROM AUTHOR]

Owing to the depletion of oil reserves and the low efficiency of traditional deep-water semi-submersible drilling platforms, the application of photovoltaic generation and energy storage systems in marine power systems has been increasingly attracting attention. However, the intermittent and uncertain nature of solar energy brings crucial challenges in maintaining stable and sustainable operations. Furthermore, unlike land-based power systems, the deep-water semi-submersible drilling platform depends on a dynamic positioning system to be fixed in an area of deep water for producing oil, but irregular and stochastic waves make the outputs of a dynamic positioning system uncertain, increasing the difficulty and complexity of energy management. This paper develops a novel hybrid day-ahead probabilistic scheduling method to reduce the total fuel cost of the whole system and improve the energy efficiency; the method combines Taguchi's orthogonal algorithm with particle swarm optimization and a probabilistic load flow method. A detailed model of the deep-water semi-submersible drilling platform is established to validate the proposed method, which is compared with various classical methods. With the help of the proposed method, the total operation cost and greenhouse gas emissions are reduced from $7562.52 to $7333.68, and from 18.95 tons to 16.81 tons, respectively. Furthermore, compared to the two-point estimation method, the experimental times drops by half, which minimizes the computational burden. The simulation results clearly demonstrate the necessity for day-ahead energy scheduling, and the advantages of the proposed method. • A photovoltaic/diesel/battery power system in a DSDP is established. • A novel hybrid day-ahead scheduling method is proposed for a DSDP. • Cumulative probability function -based Taguchi method is developed for discretization. [ABSTRACT FROM AUTHOR]

• Frequency of variations of solar irradiation is used as a feature for forecasting. • Frequency features of irradiations in two adjacent sampling windows differ little. • Principal component analysis may effectively identify crucial frequency features. • Multi-step rolling forecasting outperforms direct forecasting. • Forecasting with data in multi-site outperform that with data in a single site. Owing to a shortage of fossil fuels, environmental pollution and the greenhouse effect, renewable energy generation has become important in a modern smart grid. However, the characteristics of renewable power generation are volatile and uncertain. This work proposes a new day-ahead spatiotemporal forecasting method for solar irradiation to ensure the efficient operation of power systems. First, the frequency features of a solar irradiation time-series are extracted by discrete Fourier transform (DFT). Principal component analysis (PCA) is then used to identify the crucial frequency features, which are input to an Elman-based neural network to carry out subsequent 24-hour (day ahead) solar irradiation forecasting. Historical weather data from five weather stations that are near the target location are used. Comparative studies of traditional Autoregressive Integrated Moving Average Model (ARIMA), PCA-Back-Propagation (BP)-based neural network, the persistence method, DFT-PCA-BP and the proposed DFT-PCA-Elman method reveal that the proposed method is the most accurate in day-ahead forecasting using spatiotemporal data. [ABSTRACT FROM AUTHOR]

Owing to a shortage of fossil fuels and environmental pollution, renewable energy is gradually replacing fossil fuels in the power systems of hybrid ships. To exploit fully solar energy by the successful day-ahead scheduling of ships, this work proposes a new day-ahead spatio-temporal forecasting method. Ensemble empirical mode decomposition (EEMD) is used to extract data features and decompose original historical data into several frequency bands. After the original data are processed, data from the four land weather stations that are closest to the ship and self-organizing map-back propagation (SOM-BP) hybrid neural networks are used to forecast the solar radiation received by the ship in the next 24 h. Multiple comparative experiments are implemented. The results show that the EEMD-SOM-BP spatio-temporal forecasting method can accurately forecast the solar radiation on a ship that is sailing along a navigation route. [ABSTRACT FROM AUTHOR]

Cultural attachment (CA) suggests that cultural symbols can function as attachment figures, in a similar way to prototypical maternal attachment figures. In order to further understand the psychophysiological mechanisms of CA, we examine whether cultural symbols regulate peripheral physiological indicators of arousal in response to symbolic threats. We supraliminally expose participants to neutral or threatening stimuli, followed by the subliminal presentation of CA and control images, while recording their Skin Conductance Responses (SCR). In tandem with previous work, threat increased SCR when the subliminal image was a control. However, the subliminal presence of a cultural symbol reduced this typically high SCR to threat, potentially suggesting that the threat-related arousal was mitigated. Importantly, metrics related to the way an individual is related to the environment, i.e. the need for cognitive closure, affected physiological responses towards threat and cultural images. Overall, the present study sets the basis for potential emotional mechanisms that could explain how cultural symbols can act as extensions of the prototypical attachment figures and confer the sense of security in the face of threat. [ABSTRACT FROM AUTHOR]

Owing to low efficiency of traditional ships and the serious environmental pollution that they cause, the use of solar energy and an energy storage system (ESS) in a ship’s power system is increasingly attracting attention. However, the swinging of a ship raises crucial challenges in designing an optimal system for a large oil tanker ship, which are associated with uncertainties in solar energy. In this study, a series of experiments are performed to investigate the characteristics of a photovoltaic (PV) system on a moving ship. Based on the experimental results, an interval uncertainty model of on-board PV generation is established, which considers the effect of the swinging of the ship. Due to the power balance equations, the outputs of the diesel generator and the ESS on a large oil tanker are also modeled using interval variables. An interval optimization method is developed to determine the optimal size of the ESS in this hybrid ship power system to reduce the fuel cost, capital cost of the ESS, and emissions of greenhouse gases. Variations of the ship load are analyzed using a new method, taking five operating conditions into account. Several cases are compared in detail to demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Owing to the strict restrictions imposed by the Marine Pollution Protocol and the rapid development of renewable energy, the use of solar generation and energy storage systems in ship power systems has been increasingly attracting attention. However, the improper sizing of a hybrid power generation system in a ship power system will result in a high investment cost and increased greenhouse gas emission. This paper proposes a method for determining the optimal size of the photovoltaic (PV) generation system, the diesel generator and the energy storage system in a stand-alone ship power system that minimizes the investment cost, fuel cost and the CO 2 emissions. The power generation from PV modules on a ship relies on the date, local time, time zone, longitude and latitude along a navigation route and is different from the conditions of power systems on land. Thus, a method, which takes the seasonal and geographical variation of solar irradiations and temperatures along the route from Dalian in China to Aden in Yemen into account, for correcting the output of PV modules is developed in this paper. The proposed method considers five conditions along the navigation route to model the total ship load. Four cases are studied in details to demonstrate the applicability of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Solar energy ships came into being to reduce carbon emissions of global shipping and fossil fuels consumption. Accurate prediction of photovoltaic power generation can improve the economical operation of solar energy ships and reduce the power fluctuation of ship power systems. This study proposes a novel hybrid method to forecast ultra-short-term solar irradiation in a solar energy ship along the east coast of China. An improved graph attention framework is trained to describe the dynamic spatial-temporal topology among five weather stations and a solar energy ship. Initial interval prediction results are then determined by an improved Divided Period Optimization (DPO) method. According to distribution characteristics of the prediction error of shipboard solar irradiation, a Dynamic Prediction Interval Modification (DPIM) method is proposed to further optimize the prediction accuracy and interval width of ultra-short-term solar irradiation. Comparison experiments show that the dynamic connection matrix effectively improves the prediction accuracy, and the DPIM method reduces the PI width of 28.84%–37.79 % in shipboard solar irradiation. The proposed prediction method can accurately predict the shipboard solar irradiation at the period of significant disturbance, and provide effective technical support for the economic scheduling of solar energy ships. [Display omitted] • A new hybrid model is proposed to predict shipboard solar irradiation, especially with significant fluctuations. • A Multi-Layer Graph Attention Network (MLGAT) is improved to extract spatio-temporal feature. • An improved fluctuation-sensitivity cost function is proposed to balance the coverage rate and interval width. • A Divided Period Optimization method is put forward to rebuild the Spatio-temporal input space. • A Dynamic Prediction Interval Modification (DPIM) method is proposed to reduce the interval width of predicitons. [ABSTRACT FROM AUTHOR]

The International Maritime Organization has introduced the concept of Emission Control Area to reduce sulfur emissions from coastal areas. The expensive low-sulfur fuels are required to be used in Emission Control Area, leading to increased sulfide emissions due to bypassing by ship operators. It creates new challenges for navigation and power generation scheduling of all-electric ships. This article presents a new bi-objective joint optimization scheme for ship sailing routes and energy management. It considers emission control strategies to balance the total cost of operation and sulfur dioxide emissions throughout the ship's voyage. Unlike previous optimization problems with fixed routes and sailing time, the influence of emission policies on the operator's navigation decisions is considered. The route options and sailing time are treated as variables embedded in the model. In addition, the optimization accuracy is improved by integrating the model into mixed integer linear programming through a modified piecewise linearization model. The results show that the total operating cost for the same route is reduced by 6.05%, and the SO 2 emission is 8.36% after integrating the emission policy influencing factor in the model. The effectiveness of the proposed scheme is validated, and the model's necessity for optimizing ship power generation and voyage within Emission Control Area is also illustrated. • Route planning for ships in coastal areas is affected by emission control area. • A ship scheduling scheme considering emission control policy is established. • The variable time window flexibly optimizes the ship's voyage and energy dispatch. • The effectiveness and availability of the proposed optimization model are verified. [ABSTRACT FROM AUTHOR]

As a special mobile microgrid, an all-electric ship (AES) utilizes diesel generators and energy storage systems to provide electric propulsion and service loads. Unlike previous studies of the minimization of the AES operation using auxiliary energy storage systems, this paper exploits existing shipboard thermal storage and thermal load as a virtual energy storage system to reduce both operating cost and greenhouse gas emissions. To achieve this goal, a joint optimization model is developed optimally to coordinate the voyage scheduling and power generation of the AES under various load conditions. Thermal load and propulsion load optimization are considered in demand-side management. The problem is formulated mathematically as a multi-objective economic dispatch problem and solved by the particle swarm optimization (PSO) algorithm combined with non-dominated sorting genetic algorithm II (NSGA-II). A typical navigation route is selected for the case studies and simulation results demonstrate that the proposed joint optimization method reduces cost and greenhouse gas emissions by 17.4% and 23.6%, respectively, from those achieved using current fixed voyage generation scheduling methods. The environment friendliness and energy efficiency are further improved by coordinated penetration of the thermal storage dispatch into generation and voyage scheduling. • A mobile shipboard power system is established. • Thermal storage penetrated power management for an AES is proposed. • A joint voyage scheduling and economic dispatch is proposed for the AES. • Multi-objective problem is considered to reduce the cost and emissions of an AES. [ABSTRACT FROM AUTHOR]

Owing to the increasing concerns about the release of pollution by traditional ships, the use of the renewable energy in ships' power systems is attracting much attention. However, an improperly designed renewable generation system and energy storage system (ESS) will increase costs and greenhouse gas emissions. This paper proposes a mathematical model of a photovoltaic (PV) power generation system for a ship, taking into account the effects of ship rolling. A PV system on the shipboard, unlike one on land, has to confront dramatic power fluctuations that are caused by the motions of the ship and bad weather, so hybrid ESSs play a significant role in a ship's power system. In this work, the discrete Fourier transform (DFT) is employed to decompose the required balancing power into various time-varying periodic components, which are utilized to calculate the maximum required power of the hybrid energy storage systems. A cost analysis is performed using particle swarm optimization (PSO) algorithm to optimize the size and capacity of various types of energy storage systems. Simulation results reveal the efficiency of the optimal allocation of the ESSs. [ABSTRACT FROM AUTHOR]

In the current age of globalization, living abroad is becoming an increasingly common and highly sought after experience. Sojourners’ ability to adjust to a new culture can be affected by their existing attachments, internalized as intrapsychic environment, as well as their biological sensitivity to environment. This sensitivity can be partly attributed to one’s genomic endowments. As such, this prospective study sought to examine the differential effects of early experiences with parents and affection for home culture on young adults’ ability to adapt to a foreign culture ( n = 305, students who studied overseas for a semester) – specifically, the difficulties they experience – moderated by genetic susceptibility. An additional 258 students who did not travel overseas were included as a comparison group to demonstrate the uniqueness of intercultural adaptation. Current findings suggest that the maternal, paternal and cultural bondings or affections affect different aspects of intercultural adjustment. Maternal bonding affected sojourners’ relationships with host nationals, while paternal bonding affected sojourners’ adjustment to a new physical environment. Moreover, individuals’ genetic predispositions significantly moderate these main effects regarding how much difficulty the sojourners experienced overseas. [ABSTRACT FROM AUTHOR]

Prior research has suggested that basic individual differences in reactivity to threatening or aversive stimuli are predictors of individual differences in racial bias. Despite the role of genes in modulating such individual differences in affective and motivational responses, whether such genes contribute to individual differences in racial bias remain unknown. Here we examined the influence of the serotonin transporter polymorphism ( 5-HTTLPR ) on implicit and self-reported racial bias among a sample of European-Americans. We observed that the short-allele variant of 5-HTTLPR predicted greater levels of implicit racial biases, but not explicit self-reported racial biases. These findings demonstrate that a genetic variant involved in affective response and regulation may contribute to individual differences in the activation of automatic race-related evaluations, although this influence of genes on racial bias may not be readily expressed through deliberative self-report. [ABSTRACT FROM AUTHOR]