Abstract
Smart grids enable large-scale integration of low-carbon energy sources and energy efficiency. However, changing customer energy consumption behavior has been a challenge, requiring the development of services that change the way customers relate with energy to increase energy efficiency and savings in households. To this end, this qualitative study in the Portuguese energy market offers a nuanced understanding of how customer cocreate value with smart energy services, identifying three different customer value cocreation practice styles and respective engagement behaviors). Study findings reveal that while AHEM (Advanced Home Energy Management) and MEM (Mobility Energy Management) customers are willing to play autonomous roles in managing the energy consumption and production, HFEC (Hassle Free Home Energy Consumption) customers may be open to adopt smart energy services without spending time and effort in using these services. The study offers relevant implications for policy makers and ESCOs (energy service companies). Although much attention has been paid to advanced customers, a nuanced approach may enable ESCOs to reach disengaged customers, by offering tailored services that are suited to their hassle free value cocreation practice styles. Policy makers may also explore tailored, and service focused incentives to push the adoption of smart service solutions in large-scale.

Abstract
EUniversal project aims to facilitate the use of flexibility services and interlink distribution system's active management with electricity markets. Implementing market-based flexibility services implies a change in distribution network monitoring and control towards a more predictive approach. However, integrating cost-effective monitoring and control tools for the LV network is still quite challenging. Within the project, a set of operation and planning tools have been developed for a coordinated quantification and activation of flexibility in HV, MV and LV distribution networks. The paper presents the tools developed for the Portuguese pilot and shows preliminary results obtained when considering network operation scenarios characterized by large scale integration of DER and EV.

Abstract
Abstract Recently, research concerning the navigation of autonomous surface vehicles (ASVs) has been increasing. However, a large-scale implementation of these vessels is still held back by several challenges such as multi-object tracking. Attaining accurate object detection plays a big role in achieving successful tracking. This article presents the development of a detection model with an image-based Convolutional Neural Network trained through transfer learning, a deep learning technique. To train, test, and validate the detector module, data were collected with the SENSE ASV by sailing through two nearby ports, Leixões and Viana do Castelo, and recording video frames through its on-board cameras, along with a Light Detection And Ranging, GPS, and Inertial Measurement Unit data. Images were extracted from the collected data, composing a manually annotated dataset with nine classes of different vessels, along with data from other open-source maritime datasets. The developed model achieved a class mAP@[.5 .95] (mean average precision) of 89.5% and a clear improvement in boat detection compared to a multi-purposed state-of-the-art detector, YOLO-v4, with a 22.9% and 44.3% increase in the mAP with an Intersection over Union threshold of 50% and the mAP@[.5 .95], respectively. It was integrated in a detection and tracking system, being able to continuously detect nearby vessels and provide sufficient information for simple navigation tasks.

Abstract
Renewable energy sources such as solar and wind are now competitive with traditional fossil and nuclear power when generating but that is just the challenge. When not generating can be a problem for grid integration and the main challenge to the widespread acceptance and dissemination of solar and wind, and the focus of research for the next generation of energy engineers. Intermittent, the adjective most associated with solar and wind energy has been and continues to be the focus of research by power engineers, AI professionals, and system scientists from the late 20th century and is the critical factor in the design of the future power grids, The most obvious solution is energy storage but then the choice of the storage method and size are complex problems. Will best solutions involve pumped hydro, Li-Ion batteries, or hydrogen generation? Or will next-generation ultra-capacitors, or high-speed flywheels gyros, or some yet to be discovered device will be the dominating technologies? The primary objective of the storage designs will be based on what's best for the reliability and efficiency of the grid, and simultaneously optimizing cost and environmental impact functions. Socio-economic and geopolitical considerations must also be considered to satisfy local or regional constraints. There is also the question of purpose: will it be sized for grid stability, or medium, or long-term storage. This factor will depend on the specific grid requirements. The focus of this paper is to study multi-source renewable energy systems that include storage called HRES or Hybrid Renewable Energy with Storage. This study describes the development of a behind-the-meter Energy Management System (EMS) for an HRES, under the assumption that Real-Time Pricing (RTP) is offered by a utility supplying power to a medium-size office complex. A cost function to be minimized is introduced to optimize the contribution of each energy source. Also, this work develops the basis of a platform for decision-makers to evaluate the viability of the optimized system in conjunction with the financial feasibility analysis.

Abstract
Complex optimization problems are often associated to large search spaces and consequent prohibitive execution times in finding the optimal results. This is especially relevant when dealing with dynamic real problems, such as those in the field of power and energy systems. Solving this type of problems requires new models that are able to find near-optimal solutions in acceptable times, such as metaheuristic optimization algorithms. The performance of these algorithms is, however, hugely dependent on their correct tuning, including their configuration and parametrization. This is an arduous task, usually done through exhaustive experimentation. This paper contributes to overcome this challenge by proposing the application of sequential model algorithm configuration using Bayesian optimization with Gaussian process and Monte Carlo Markov Chain for the automatic configuration of a genetic algorithm. Results from the application of this model to an electricity market participation optimization problem show that the genetic algorithm automatic configuration enables identifying the ideal tuning of the model, reaching better results when compared to a manual configuration, in similar execution times.

Abstract
For an efficient and reliable operation of an Airborne Wind Energy System, it is widely accepted that the kite should follow a pre-defined optimized path. In this article, we address the problem of designing a trajectory controller so that such path is closely followed. The path-following controllers investigated are based on a well-known nonlinear guidance logic termed L1 and on a proposed modification of it, which we termed L0. We have developed and implemented both L0 and L1 controllers for an AWES. The two controllers have an easy implementation with an explicit expression for the control law based on the cross-track error, on the heading angle relative to the path, and on a single parameter L (L-0 or L-1, depending on each controller) that we are able to tune. The L0 controller has an even easier implementation since the explicit control law can be used without the need to switch controllers. Since the switching of controllers might jeopardize stability, the L-0 controller has an important theoretical advantage in being able to guarantee stability on a larger domain of attraction. The simulation study shows that both nonlinear guidance logic controllers exhibit appropriate performance when the L parameter is adequately tuned, with the L0 controller showing a better performance when measured in terms of the average cross-track error.