This research study deals with the analysis of climate catastrophes that have occurred worldwide and in what measure they have affected communities, in an economic and social point of view, and energy markets in general over time. A chronological sweep across Europe has been carried out in order to evaluate the different consequences that phenomena such as hurricanes, winter storms, and floods have had, especially on the energy prices. Moreover, the effects of the variability of renewable generation during climate disasters not only in Europe but also in North America, Australia, and Latin America have been discussed. Furthermore, best practices and novel strategies for climate adaptations have been identified in different countries, and the results show how energy planning integrated within a systematic diversification of energy sources and investment in infrastructure and advanced technologies such as distributed generation and digital twins can be crucial to enhance the reliability of energy systems.

Climate change and global warming are causing growing environmental concerns, prompting many countries to increase investments in renewable energies. The high growth rate of renewables in the energy systems brings significant intermittency challenges. Demand-side flexibility is presented as a viable solution to address this phenomenon. In this framework, this research study proposes a novel methodology to evaluate the flexibility potential that residential consumers can offer to the Distribution System Operator (DSO). Moreover, it pretends to provide guidelines and design of standardized parameters to disaggregate the aggregated energy consumption data of end-users. This step is essential to identify and characterize the primary energy consumption processes in the residential sector, laying the groundwork for future flexibility evaluation. Furthermore, the modeling of the energy consumption curves will enhance residential sector demand-side flexibility enabling end-users to modify their usual consumption patterns. The implemented methodology has been applied to real consumer data provided by the DSO of a Spanish municipality of about 29,000 habitants in the Alicante Province (Spain). Results achieved allowed the validation of the proposed methodology enabling the disaggregation of residential energy profiles and facilitating the subsequent dynamic assessment of residential end-user’s demand flexibility. Moreover, this work will provide valuable guidelines to carry out short-term energy resource planning and solve operational problems of the energy systems.

In the so-called society 4.0, Artificial Intelligence (AI) is being widely used in many areas of life. Machine learning uses mathematical algorithms based on "training data", which are able to make predictions or take decisions with the ability to change their behavior through a self-training approach. Furthermore, thanks to AI, a large volume of data can be now processed with the overall goal to extract patterns and transform the information into a comprehensible structure for further utilization, which manually done by humans would easily take several years. In this framework, this article explores the potential of AI and machine learning to empower flipped classroom with just-in-time teaching (JiTT). JiTT is a pedagogical method that can be easily combined with the reverse teaching. It allows professors to receive feedback from students before class, so they may be able to adapt the lesson flow, as well as preparing strategies and activities focused on the student deficiencies. This research explores the application of AI in high education as a tool to analyze the key variables involved in the learning process of students and to integrate JiTT within the flipped classroom. Finally, a case of application of this methodology is presented, applied to the course of Industrial Refrigeration taught at the Polytechnic University of Valencia.