Abstract
The worldwide investment in renewable energy sources is leading to the formation of local energy communities in which users can trade electric energy locally. Regulations and the required enablers for effective transactions in this new context are currently being designed. Hence, the development of software tools to support local transactions is still at an early stage and faces the challenge of constant updates to the data models and business rules. The present paper proposes a novel approach for the development of software tools to solve auction-based local electricity markets, considering the special needs of local energy communities. The proposed approach considers constrained bids that can increase the effectiveness of distributed generation use. The proposed method takes advantage of semantic web technologies, in order to provide models with the required dynamism to overcome the issues related to the constant changes in data and business models. Using such techniques allows the system to be agnostic to the data model and business rules. The proposed solution includes the proposed constraints, application ontology, and semantic rule templates. The paper includes a case study based on real data that illustrates the advantages of using the proposed solution in a community with 27 consumers.

Abstract
Determining the PSF remains a key challenge for post adaptive-optics (AO) observations regarding the spatial, temporal and spectral variabilities of the AO PSF, as well as itx complex structure. This paper aims to provide a non-exhaustive but classified list of techniques and references that address this issue of PSF determination, with a particular scope on PSF reconstruction, or more generally pupil-plane-based approaches. We have compiled a large amount of references to synthesize the main messages and kept them at a top level. We also present applications of PSF reconstruction/models to post-processing, more especially PSF-fitting and deconvolution for which there is a fast progress in the community. © 2020 SPIE.

Abstract
Monocular Visual Odometry techniques represent a challenging and appealing research area in robotics navigation field. The use of a single camera to track robot motion is a hardware-cheap solution. In this context, there are few Visual Odometry methods on the literature that estimate robot pose accurately using a single camera without any other source of information. The use of omnidirectional cameras in this field is still not consensual. Many works show that for outdoor environments the use of them does represent an improvement compared with the use of conventional perspective cameras. Besides that, in this work we propose an open-source monocular omnidirectional version of the state-of-the-art method Libviso2 that outperforms the original one even in outdoor scenes. This approach is suitable for central dioptric omnidirectional cameras and takes advantage of their wider field of view to calculate the robot motion with a really positive performance on the context of monocular Visual Odometry. We also propose a novel approach to calculate the scale factor that uses matches between laser measures and 3-D triangulated feature points to do so. The novelty of this work consists in the association of the laser ranges with the features on the omnidirectional image. Results were generate using three open-source datasets built in-house showing that our unified system largely outperforms the original monocular version of Libviso2.

Abstract
A new probabilistic approach for microgrids (MGs) optimal energy management considering ac network constraints is proposed in this paper. The economic model of an energy storage system (ESS) is considered in the problem. The reduced unscented transformation (RUT) is applied in order to deal with the uncertainties related to the forecasted values of load demand, market price, and available outputs of renewable energy sources (RESs). Moreover, the correlation between market price and load demand is taken into account. Besides, the impact of the correlated wind turbines (WT) on MGs' energy management is studied. An enhanced JAYA (EJAYA) algorithm is suggested to achieve the best solution of the considered problem. The effective performance of the presented approach is verified by applying the suggested strategy on a modified IEEE 33-bus system. It can be observed that for dealing with probabilistic problems, the suggested RUT-EJAYA shows accurate results such as those of Monte Carlo (MC) while the computational burden (time and complexity) is lower.

Abstract
Tackling multi-agent environments where each agent has a local limited observation of the global state is a non-trivial task that often requires hand-tuned solutions. A team of agents coordinating in such scenarios must handle the complex underlying environment, while each agent only has partial knowledge about the environment. Deep reinforcement learning has been shown to achieve super-human performance in single-agent environments, and has since been adapted to the multi-agent paradigm. This paper proposes A3C3, a multi-agent deep learning algorithm, where agents are evaluated by a centralized referee during the learning phase, but remain independent from each other in actual execution. This referee's neural network is augmented with a permutation invariance architecture to increase its scalability to large teams. A3C3 also allows agents to learn communication protocols with which agents share relevant information to their team members, allowing them to overcome their limited knowledge, and achieve coordination. A3C3 and its permutation invariant augmentation is evaluated in multiple multi-agent test-beds, which include partially-observable scenarios, swarm environments, and complex 3D soccer simulations.

Abstract
This work proposes a new control framework for power converters with a dual half bridge (DHB) configuration. The new framework exploits the multi-port structure of the DHB to simultaneously: i) regulate the current in the primary side of the DIM and ii) equalize the voltage in the two secondary ports of the DHB. To implement these functions, we combine input-output linearization methods with pragmatic voltage balance algorithms. We then apply this framework to a hybrid energy storage system composed of a battery pack and two supercapacitor modules. Numerical simulation results demonstrate the effectiveness of the proposed approach in regulating the power between the energy storage units and balancing the supercapacitors' voltages.