A Scalable Network-Aware Multi-Agent Reinforcement Learning Framework for Decentralized Inverter-based Voltage Control

This paper addresses the challenges associated with decentralized voltage control in power grids due to an increase in distributed generations (DGs). Traditional model-based voltage control methods struggle with the rapid energy fluctuations and uncertainties of these DGs. While multi-agent reinforcement learning (MARL) has shown potential for decentralized secondary control, scalability issues arise when dealing with a large number of DGs. This problem lies in the dominant centralized training and decentralized execution (CTDE) framework, where the critics take global observations and actions. To overcome these challenges, we propose a scalable network-aware (SNA) framework that leverages network structure to truncate the input to the critic's Q-function, thereby improving scalability and reducing communication costs during training. Further, the SNA framework is theoretically grounded with provable approximation guarantee, and it can seamlessly integrate with multiple multi-agent actor-critic algorithms. The proposed SNA framework is successfully demonstrated in a system with 114 DGs, providing a promising solution for decentralized voltage control in increasingly complex power grid systems

Revisiting customer loyalty toward mobile e-commerce in the hospitality industry: does brand viscosity matter?

Purpose To better understand how to retain hospitality customers in the fierce competition among mobile applications, this study aims to propose and empirically validates an integrative framework, which elaborates how conscious and subconscious factors, together with affective factors, may induce app loyalty and how brand viscosity moderates such effects. Design/methodology/approach The authors conducted an online survey to collect data and received a total of 268 valid responses. This study splits the data into two groups (brand viscosity vs non-viscosity). Then, the authors performed a multi-group structural equation modeling with Chi-square difference tests to compare the model between the two groups. Findings The findings support the integrative model and reveal that the influence of app satisfaction on loyalty is stronger for app users who do not stick to one brand across the website and mobile app channels. Moreover, for those with brand viscosity, habit and switching cost are two significant determinants that exert positive effects in inducing app loyalty. Research limitations/implications Brand viscosity across different channels matters for the effects of habit and switching costs in shaping app loyalty. E-commerce managers should elaborate on brand management among various booking channels and establish effective digital marketing strategies to facilitate the formation of usage habits and switching costs and to enhance brand viscosity across channels. Originality/value This research advances the knowledge of app loyalty in hospitality by providing a comprehensive explanatory framework from affective, conscious and subconscious lenses. This research is among the first to unveil the impact of brand viscosity on the links between loyalty and its determinants

Accurate Time-segmented Loss Model for SiC MOSFETs in Electro-thermal Multi-Rate Simulation

Compared with silicon (Si) power devices, Silicon carbide (SiC) devices have the advantages of fast switching speed and low on-resistance. However, the effects of non-ideal characteristics of SiC MOSFETs and stray parameters (especially parasitic inductance) on switching losses need to be further evaluated. In this paper, a transient loss model based on SiC MOSFET and SiC Schottky barrier diode (SBD) switching pairs is proposed. The transient process analysis is simplified by time segmentation of the transient process of power switching devices. The electro-thermal simulation calculates the junction temperature and updates the temperature-related parameters with the proposed loss model and the thermal network model. A multi-rate data exchange strategy is proposed to solve the problem of disparity in timescales between circuit simulation and thermal network simulation. The CREE CMF20120D SiC MOSFET device is used for the experimental verification. The experimental results verify the accuracy of the model which provides guidance for the circuit design of SiC MOSFETs. All the parameters of the loss model can be extracted from the datasheet, which is practical in power electronics design

Numerical Derivative-based Flexible Integration Algorithm for Power Electronic Systems Simulation Considering Nonlinear Components

Simulation is an efficient tool in the design and control of power electronic systems. However, quick and accurate simulation of them is still challenging, especially when the system contains a large number of switches and state variables. Conventional general-purpose integration algorithms assume nonlinearity within systems but face inefficiency in handling the piecewise characteristics of power electronic switches. While some specialized algorithms can adapt to the piecewise characteristics, most of these methods require systems to be piecewise linear. In this article, a numerical derivative-based flexible integration algorithm is proposed. This algorithm can adapt to the piecewise characteristic caused by switches and have no difficulty when nonlinear non-switching components are present in the circuit. This algorithm consists of a recursive numerical scheme that obtains high-order time derivatives of nonlinear components and a decoupling strategy that further increases computational efficiency. The proposed method is applied to solve a motor derive system and a large-scale power conversion system (PCS) to verify its accuracy and efficiency by comparing experimental waveforms and simulated results given by commercial software. Our proposed method demonstrates several-fold acceleration compared to multiple commonly used algorithms in Simulink.Comment: 10 pages, 8 figure

Characteristics and Mitigation Measures of Aircraft Pollutant Emissions at Nanjing Lukou International Airport (NKG), China

The assessment of local air pollution due to aircraft emissions at/near the airport is an important issue from the standpoint of environment and human health, but has not received due attention in China. In this paper, the pollutant emissions (i.e. HC, CO, NOx, SOx and PM) from aircraft during landing and take-off (LTO) cycles at Nanjing Lukou Airport (NKG) in 2016 were investigated using an improved method, which considered the taxi-in and –out time calculated based on the real data from the Civil Aviation Administration of China (CAAC), instead of using the referenced time recommended by ICAO. First, the pollutant emissions and their characteristics were studied from different perspectives. Second, two various mitigation measures of emissions were proposed, and the performance of emission reduction was analysed. Our analysis shows that: (1) A320 and B738 emitted the largest emissions at NKG; (2) pollutants were mainly emitted during the taxi mode, followed by climb mode; (3) B738 had the lowest emissions per (seat•LTO) among all aircraft, while CRJ had the lowest emissions per unit LTO; (4) shortening the taxiing time and upgrading aircraft engines are both effective measures to mitigate pollutant emissions.</p

Towards A Robust Group-level Emotion Recognition via Uncertainty-Aware Learning

Group-level emotion recognition (GER) is an inseparable part of human behavior analysis, aiming to recognize an overall emotion in a multi-person scene. However, the existing methods are devoted to combing diverse emotion cues while ignoring the inherent uncertainties under unconstrained environments, such as congestion and occlusion occurring within a group. Additionally, since only group-level labels are available, inconsistent emotion predictions among individuals in one group can confuse the network. In this paper, we propose an uncertainty-aware learning (UAL) method to extract more robust representations for GER. By explicitly modeling the uncertainty of each individual, we utilize stochastic embedding drawn from a Gaussian distribution instead of deterministic point embedding. This representation captures the probabilities of different emotions and generates diverse predictions through this stochasticity during the inference stage. Furthermore, uncertainty-sensitive scores are adaptively assigned as the fusion weights of individuals' face within each group. Moreover, we develop an image enhancement module to enhance the model's robustness against severe noise. The overall three-branch model, encompassing face, object, and scene component, is guided by a proportional-weighted fusion strategy and integrates the proposed uncertainty-aware method to produce the final group-level output. Experimental results demonstrate the effectiveness and generalization ability of our method across three widely used databases.Comment: 11 pages,3 figure