Abstract
Electric mobility has been one of the big bets for the reduction of CO2 in the transport sector. But, the integration of electric vehicles on a large scale, especially the charging of their battery will bring some challenges in the distribution of electricity to avoid problems in their transport. In this paper, the impact of introducing electric vehicle charging stations and renewable energy sources in a 69-node IEEE network will be analysed. The integration of charging stations into the grid leads to high losses and voltage drops that harm the network. On the other hand, the installation of Photovoltaic (PV) panels, besides the advantage of energy production, improves the profile of the grid in terms of voltage drops. The choice of the best location for the charging stations, as well as the best location for the renewable sources, is made using two genetic algorithms. The results obtained show that the genetic algorithms can solve the problem efficiently. © 2023 IEEE.

Abstract
As the world's population grows, there is a need to find new sources of energy that are more sustainable. Photovoltaic (PV) energy is one of the renewable energy sources (RES) expected to have the greatest margin for growth in the near future. Given their intermittency, RES bring uncertainty and instability to the management of the power system, therefore it is essential to predict their behavior for different time frames. This paper aims to find the most effective forecasting method for PV energy production that could be applied to different time frames. PV energy production is directly dependent on solar radiation and temperature. Several forecasting approaches are proposed in this paper. A multiple linear regression (MLR) model is proposed to predict the monthly energy production based on the climatic parameters of the previous year. Different approaches are proposed based on first predicting the temperature and radiation and then applying the PV mathematical models to predict the produced energy. Three methods are proposed to predict the climatic parameters: using the average values, the additive decomposition, or the Holt-Winters method. Comparing the errors of the four proposed forecasting methods, the best model is the Holt-Winters, which presents smaller errors for radiation, temperature, and produced energy. This method is close to additive decomposition. © 2023 IEEE.

Abstract
Renewable energy communities (REC) are an increasingly interesting solution for all energy market stakeholders. In RECs consumers and producers come together to form energy cooperatives with a strong incorporation of renewables in order to make the market and energy trading more advantageous for both sides. This growing trend has been followed by several studies aimed at understanding which are the best models for energy sharing within the community. This paper proposes different models of energy sharing within the community and evaluates their efficiency. Energy sharing can be based on constant coefficients or variable coefficients based on the net consumption of the self-consumers. This study proposes a new methodology based on a hybrid model. The results show the advantages and challenges of the individual energy-sharing models, showing that up to 41% of the energy imports from the grid can be reduced. © 2023 IEEE.

Abstract
The recent pandemic crisis has greatly impacted startups, and some changes are expected to be long-lasting. Small businesses usually have fewer resources and are more vulnerable to losing customers and investors, especially during crises. This study investigates how startups’ business processes were affected and how entrepreneurs managed this sudden change brought by the COVID-19 outbreak. Data were analyzed using qualitative research methods through in-depth interviews with the co-founders of eighteen startups. Results show that the three core business processes affected by the COVID-19 crisis were marketing and sales, logistics and operations, and organizational support. The way to succeed is to be flexible, agile, and adaptable, with technological knowledge focusing on digital channels to find novel opportunities and innovate. Additionally, resilience, self-improvement, education, technology readiness and adoption, close relationship with customers and other stakeholders, and incubation experience seem to shield startups against pandemic crisis outbreaks.

Abstract
Marketing, and specifically its digital marketing component, is being challenged by disruptive innovations, which are creating new, unique, and unusual opportunities, and with the emergence of new paradigms and models. Other areas of knowledge have embraced these innovations with swiftness, adapting promptly and using them as leverage to create new paradigms, models, and realities. Marketing, in clear opposition, has been somewhat dismissive, ignoring the potential of these new contexts that are emerging, some of which are already unavoidable. Confronting Security and Privacy Challenges in Digital Marketing identifies the most relevant issues in the current context of digital marketing and explores the implications, opportunities, and challenges of leveraging marketing strategies with digital innovations. This book explores the impact that these disruptive innovations are having on digital marketing, pointing out guidelines for organizations to leverage their strategy on the opportunities created by them. Covering topics such as blockchain technology, artificial intelligence, and virtual reality, this book is ideal for academicians, marketing professionals, researchers, and more. © 2023 by IGI Global. All rights reserved.

Abstract
The internet massification and e-commerce growth that have been driven by “millennials” and the coronavirus pandemic cannot remain indifferent to luxury brands. These brands have had to adapt to e-commerce and develop an online shopping experience which satisfies its customers, so that they repeat purchase. Therefore, the main objective of this research is to understand the main impacts of shopping experience on luxury brand websites on satisfaction and loyalty. A model which analyzes the relationship between the three constructs was developed and information was gathered through an online survey, from which resulted 356 valid answers. Through the analysis of data collected and using a structural equation model, using SmartPLS software, we realized that online shopping experience is positively related to satisfaction. Loyalty, in turn, is positively affected by brand satisfaction. This study makes an important contribution to luxury brands and to people in charge of marketing and online platforms selling luxury goods. It helps brands understand that enhancing online shopping experience can positively impact satisfaction and loyalty levels. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.