Abstract
Europe and, more particularly, the European Union (EU) has been pursuing ambitious goals in terms of energy, with pioneering energy policy pushing for more clean and affordable energy and highly competitive electricity markets. Electricity market design proved to be a challenge since the first models intended for further competition in the sector have been launched. With the increasing use of distributed and renewable-based electricity generation, electricity models became increasingly challenging. Other distributed energy resources, namely demand flexibility, distributed storage, and electric vehicles, are also bringing new requirements for electricity markets and open the way for local electricity markets. Although still an emerging concept, local electricity markets have huge potential, namely regarding increased gathering of the demand flexibility potential and to bring significant benefits to consumers. This chapter addresses the EU vision for electricity markets in the new context and discusses its benefits, risks, and future perspectives, highlighting the most important legislation, and some practical advances and implementations. © 2021 Elsevier Inc.

Abstract

Abstract
Optimizing software to become (more) energy efficient is an important concern for the software industry. Although several techniques have been proposed to measure energy consumption within software engineering, little work has specifically addressed Software Product Lines (SPLs). SPLs are a widely used software development approach, where the core concept is to study the systematic development of products that can be deployed in a variable way, e.g., to include different features for different clients. The traditional approach for measuring energy consumption in SPLs is to generate and individually measure all products, which, given their large number, is impractical. We present a technique, implemented in a tool, to statically estimate the worst-case energy consumption for SPLs. The goal is to reason about energy consumption in all products of a SPL, without having to individually analyze each product. Our technique combines static analysis and worst-case prediction with energy consumption analysis, in order to analyze products in a feature-sensitive manner: a feature that is used in several products is analyzed only once, while the energy consumption is estimated once per product. This paper describes not only our previous work on worst-case prediction, for comprehensibility, but also a significant extension of such work. This extension has been realized in two different axis: firstly, we incorporated in our methodology a simulated annealing algorithm to improve our worst-case energy consumption estimation. Secondly, we evaluated our new approach in four real-world SPLs, containing a total of 99 software products. Our new results show that our technique is able to estimate the worst-case energy consumption with a mean error percentage of 17.3% and standard deviation of 11.2%.

Abstract
Archives are evolving. Analog archives are becoming increasingly digitized and linked with other cultural heritage institutions and information sources. Diverse forms of born-digital archives are appearing. This diversity asks for systematic ways to characterize existing archives managing physical or digital records. We conducted a systematic review to identify and understand how archives are characterized. From the 885 identified articles, only 15 were focused on archives’ characterization and, therefore, included in the study. We found several characterization features organized in three main groups: archival materials, provided services, and internal processes.

Abstract
Engineering education, the process of teaching knowledge and principles to the professional practice of engineering, can be done by resorting to several methodologies. Project Based Learning is a teaching method that allows students to get knowledge and skills by developing and solving complex problems or challenges, supported by a supervisor. In the presented work, a real airplane cockpit development is used as a case study for Mechanical, Mechatronics, Electrical, and Computer Science courses. Students are encouraged to develop modules to be applied in the cockpit and further integrated with other ones.

Abstract
In the recent past structural changes in the operation and topology of the electrical system have occurred. These changes have coincided with the emergence of distributed energy resources (DERs). Relating to supply side technologies, distributed generation (DG) units have become increasingly common. The demand side has also seen the growth of new technological applications, including electric vehicles (EVs). These changes to the electrical system are being especially felt at the low voltage network level. Technical Virtual Power Plants (TVPPs) have been used to optimally schedule these DERs to increase the network flexibility and at the same time increasing the reliability and power quality of the network and this can bring economic benefits to both the TVPP operator and the customer. This paper develops a stochastic mixed-integer linear programming (MILP) optimization model to maximize the profit of a TVPP. The main objective of the TVPP is to increase operational flexibility of the low voltage network by aggregating DERs, including DG units, Heating Ventilation and Air Conditioning units, and EVs. The model is examined through the use of the IEEE 119-Bus test system. Results demonstrate that the inclusion of DG units and EVs, the profit of the TVPP increases by approximately 45% and system flexibility is increased while respecting the technical constraints of the network and the thermal comfort of the consumers.