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.

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Abstract
ABSTRACT: The subject matter of this report is the analysis of the electricity markets’ actors’ scene, through the identification of actor classes and the characterisation of actors from a behavioural and an operational perspective. The technoeconomic characterization of market participants aims to support the upcoming model enhancements by aligning the agent-based model improvements with the modern market design challenges and the contemporary characteristics of players. This work has been conducted in the context of task T3.2, which focuses on the factorization of the distinctive operational and behavioural characteristics of players in market structures. Traditional parties have been considered together with new and emerging roles, while special focus has been given on new actors related to flexible technologies and demand-side response. Among the main objectives have been the characterization of individual behaviours, objectives and requirements of different electricity market players, considering both the traditional entities and the new distributed ones, and the detailed representation of the new actors.

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.

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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.