Abstract
Many Generation Expansion Problems (GEP) models have been proposed in the literature based on agent-based equilibria or cost-minimization, integrated in bilevel or single-level models. In the simplest (and unrealistic) single-level cost minimization GEP with only the balance constraint, it can be proved that optimal generation investments are recovered through the system marginal cost, meaning that the Net Present Value (NPV) is 0. However, in more complex representations with additional constraints (such as technical or minimum capacity system constraints) non-profitable investments might occur, i.e., their NPV can go below 0. The aim of this work is to provide insights on how introducing complexity into GEP models affects the investments with and without imposing positive NPV as new constraints. The non-linearities in the NPV formulation are solved with a novel iterative algorithm. The main conclusion from the case studies is that the cost minimization GEP model forcing positive NPV can help to better represent the behavior of energy market players and simulate oligopolistic energy markets without explicitly representing profit maximization.

Abstract
This paper describes an open standards-based information system that can support the democratization and consumer empowerment through flexibility activation in the distribution networks of the near future. The paper outlines a software infrastructure focused on technical issues, closely following the OpenADR standard and the corresponding IEC 62746-10 standard. The infrastructure represents a communication backbone allowing the connection, registering, activation and reporting for different types of granular consumer flexibility. The flexibility sources can be very diverse - from controllable charging set points of electric vehicle chargers and district-level storages such as stationary batteries, towards taking advantage of comparatively large time constants of thermal systems in residential and commercial buildings. In the viewpoint of the proposed system, all these flexibility provisions represent distributed energy resources in a wider sense. The system thus offers interoperable support for consumer-level integration of different energy systems (electricity, heat and gas), and additional flexibility sources are made available to the electric power system, all the time keeping the user comfort and avoiding service disruptions. The paper outlines the technical infrastructure as a backbone activating new sources of flexibility, helping the further proliferation of renewable energy sources and establishing new market actors.

Abstract
Electricity markets are evolving into a local trading setting, which makes it for unexperienced players to achieve good agreements and obtain profits. One of the solutions to deal with this issue is to provide players with decision support solutions capable of identifying opponents' negotiation profiles, so that negotiation strategies can be adapted to those profiles in order to reach the best possible results from negotiations. This paper presents an approach that classifies opponents' proposals during a negotiation, to determine which is the typical negotiation profile in which the opponent most relates. The classification process is performed using an artificial neural network approach, and it is able to adapt at each new proposal during the negotiation process, by re-classifying the opponents' negotiation profile according to the most recent actions. In this way, effective decision support is provided to market players, enabling them to adapt the negotiation strategy throughout the negotiations. © 2019 IEEE.

Abstract
The modem societies claim on being safer, more sustainable and with more inclusive mobility policies, towards assuring the world sustainable standards. One of the key future mobility factors is focused on the usage of the soft modes, emphasizing the use of bicycles. This mean of transport needs to be permanently implemented worldwide, supported by policies that can unify user's, planners, public and private companies, academics and politicians. Therefore, it is crucial to understanding the user's motivations to adopt the bicycle as a mean of transport instead of the motorized vehicle solutions. With the aim of understanding the user's characteristics, needs and expectations regarding the use of bicycle, as well as to point out innovative initiatives and the role of bicycles in cities' planning, some European examples were analyzed and described as state-of-the-art practices. Such examples allow us to consider and propose a set of adjustments and future recommendations to strengthen the bicycle option as an effective mass mobility mean.

Abstract
Among the most complex problems in the field of 2-dimensional cutting & packing are irregular packing problems, in which items may have a more complex geometry. These problems are prominent in several areas, including, but not limited to, the textile, shipbuilding and leather industries. They consist in placing a set of items, whose geometry is often represented by simple polygons, into one or more containers such that there is no overlap between items and the utility rate of the container is maximized. In this work, the irregular strip packing problem, an irregular packing variant with a variable length container, is investigated. The placement space is reduced by adopting a rectangular grid and a full search is performed using preprocessed raster penetration maps to efficiently determine the new position of an item. Tests were performed using simple dotted board model cases and irregular strip packing benchmark instances. The comparison of our results with the state of the art solutions showed that the proposed algorithm is very competitive, achieving better or equal compaction in 9 out of 15 instances and improving the average density in 13 instances. Besides the contribution of the new best results, the proposed approach showed the advantage of adopting discrete placement, which can be potentially applied to other irregular packing problems.

Abstract
Collecting product and process measures in software development projects, particularly in education and training environments, is important as a basis for assessing current performance and opportunities for improvement. However, analyzing the collected data manually is challenging because of the expertise required, the lack of benchmarks for comparison, the amount of data to analyze, and the time required to do the analysis. ProcessPAIR is a novel tool for automated performance analysis and improvement recommendation; based on a performance model calibrated from the performance data of many developers, it automatically identifies and ranks potential performance problems and root causes of individual developers. In education and training environments, it increases students' autonomy and reduces instructors' effort in grading and feedback. In this article, we present the results of a controlled experiment involving 61 software engineering master students, half of whom used ProcessPAIR in a Personal Software Process (PSP) performance analysis assignment, and the other half used a traditional PSP support tool (Process Dashboard) for performing the same assignment. The results show significant benefits in terms of students' satisfaction (average score of 4.78 in a 1-5 scale for ProcessPAIR users, against 3.81 for Process Dashboard users), quality of the analysis outcomes (average grades achieved of 88.1 in a 0-100 scale for ProcessPAIR users, against 82.5 for Process Dashboard users), and time required to do the analysis (average of 252 min for ProcessPAIR users, against 262 min for Process Dashboard users, but with much room for improvement).