Abstract

Abstract
Hydrogen vehicle stations (HVSs) that convert electricity into hydrogen have appeared as a new arrival asset to the power system with the raising interest in hydrogen vehicles (HVs). In order to safely power these new assets, microgrids, including different flexible resources, are an ideal option. This paper presents an efficient MG scheduling in the presence of HVSs, renewable energy resources, energy storage systems (ESS) and demand response. This model also takes the uncertainties associated with electrical loads, renewables, and HVs into consideration. In order to create an MILP problem, linearized AC optimal power flow equations are considered. A 21-bus MG is examined by applying the proposed model to various case studies, thereby proving that the MG schedule meets the demand of HVs and electrical load. Employing DR programs can reduce operation costs and reduce the load during peak usage hours. Furthermore, the physical constraints of the network satisfy the security in operation. Finally, numerical analysis illustrates the effectiveness of the proposed method.

Abstract
The development of robotic systems to operate in forest environments is of great relevance for the public and private sectors. In this sense, this article reviews several scientific papers, research projects and commercial products related to robotic applications for environmental preservation, monitoring, wildfire firefighting, inventory operations, planting, pruning and harvesting. After conducting critical analysis, the main characteristics observed were: (a) the locomotion system is directly affected by the type of environmental monitoring to be performed; (b) different reasons for pruning result in different locomotion and cutting systems; (c) each type of forest, in each season and each type of soil can directly interfere with the navigation technique used; and (d) the integration of the concept of swarm of robots with robots of different types of locomotion systems (land, air or sea) can compensate for the time of executing tasks in unstructured environments. Two major areas are proposed for future research works: Internet of Things (IoT)-based smart forest and navigation systems. It is expected that, with the various characteristics exposed in this paper, the current robotic forest systems will be improved, so that forest exploitation becomes more efficient and sustainable.

Abstract
This research provides an overview of the online consumer experience of luxury brands in Portugal. The purpose of this study was to identify the significant factors that represent customers’ perceptions of the online shopping experience for luxury products. Using a quantitative approach, the authors conducted an online survey. 327 usable responses were obtained. Descriptive and factorial statistical analyzes were used to provide the empirical findings. This study proposes and empirically tests a model of the factorial structure of the online shopping experience for luxury goods. We found an eight-factor dimension structure that proposes the main contributors to understand the factors that represent consumer perceptions about buying luxury products online. The findings suggest that the eight ranked significant factors that represent the customer’s perception of the online luxury shopping experience are in this order: e-buying experience, e-loyalty, e-risk, e-satisfaction, luxury value, luxury useless, luxury future buy, and e-buying influence. The work provides empirical evidence that the eight significant factors represent the customer’s perception of the luxury shopping experience online, that help to understand how luxury brands should be managed online in order to enhance customer e-buying experience, e-satisfaction, e-loyalty, and luxury value proposition. This study provides several contributions for online luxury brand managers and some directions for further research. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Abstract
We study last-mile delivery with the option of crowd shipping, where a company makes use of occasional drivers to complement its vehicle's fleet in the activity of delivering products to its customers. We model it as a data-driven distributionally robust optimization approach to the capacitated vehicle routing problem. We assume the marginals of the defined uncertainty vector are known, but the joint distribution is difficult to estimate. The presence of customers and available occasional drivers can be random. We adopt a strategic planning perspective, where an optimal a priori solution is calculated before the uncertainty is revealed. Therefore, without the need for online resolution performance, we can experiment with exact solutions. Solving the problem defined above is challenging: not only the first-stage problem is already NP-Hard, but also the uncertainty and potentially the second-stage decisions are binary of high dimension, leading to non-convex optimization formulations that are complex to solve. We propose a branch-price-and-cut algorithm taking into consideration measures that exploit the intrinsic characteristics of our problem and reduce the complexity to solve it.

Abstract
There are significant changes occurring both in the electricity system and the natural gas system. These two energy carries can be combined to form what is known as an energy hub. These energy hubs can play a significant role in the energy system and thus understanding of their optimization, especially their costs, is important. This paper proposes a risk management framework for an energy-hub through the utilization of the information-gap decision theory (IGDT). The uncertainties introduced from the various load profiles, such as the electric and heating loads, are considered in this risk management framework. The modeled energy-hub consists of several distributed generation systems such as a microcombined heat and power (mu CHP), electric heat pump (EHP), electric heater (EH), absorption chiller (AC) and an energy storage system (ESS). A demand response (DR) program is also considered to shift a percentage of electric load away from the peak period to minimize the operational cost of the hub. A feasible test system is also applied to demonstrate the proposed model's effectiveness.