Abstract
This article provides a framework for coordinating the operation of multiple microgrids with hydrogen systems in a distribution network considering the uncertainties of wind and solar power generation as well as load demands. The model is based upon a bilevel stochastic programming problem. On the upper level, the distribution system is the leader with a profit-maximization goal, and the microgrids are followers with cost-minimization goals on the lower level. The problem is solved by transforming the model to a single-level model using Karush-Kuhn-Tucker (KKT) conditions and linearized using McCormick's relaxation and Fortuny-Amat techniques. Unlike previous studies, both levels are modeled as scenario-based stochastic problems. Moreover, the scenarios associated with uncertain variables are obtained from a real data set. After preparing the data set, scenarios are reduced using a machine learning-based clustering approach. An application of the coordinated operation model is developed for a distribution network containing several microgrids. By solving the problem, the optimal amount of power exchange and the clearing price between microgrids and distribution systems are determined. Moreover, the proposed bilevel model made 13% more profit for the distribution system than the centralized model. Also, the effects of integrating hydrogen systems with microgrids on increasing the flexibility of operators are investigated.

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This essay addresses the importance of geotechnics and marine georesources in developing technologies. The study proposes a coastal and marine geoengineering approach that focuses on five domains (5G’s): georesources, geology, geotechnics, geomechanics and geotechnologies. These domains are considered in several engineering projects related to coastal, hydraulic, marine, and maritime applications. The reality of a world in rapid, continuous evolution has put into perspective a multidisciplinary approach combining different practices and coastal strategies. That is the main driving force that should be considered when dealing with geoengineering projects in general. Moreover, geological resources and the potential exploitation of mineral and rock raw materials boost innovative techniques and upscaling geo-robotic systems. Finally, the essay unravels new trends in challenging and extreme environments and natural hazards, highlighting emerging fields such as deep-sea mining, geo-exploration, offshore, coastal, harbour and marine geotechnics. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
There is an increasing need for autonomous mobile robots (AMRs) in industrial environments. The capability of autonomous movement and transportation of items in industrial environments provides a significant increase in productivity and efficiency. This need, coupled with the possibility of controlling groups of heterogeneous robots, simultaneously addresses a wide range of tasks with different characteristics in the same environment, further increasing productivity and efficiency. This paper will present an implementation of a system capable of coordinating a fleet of heterogeneous robots with robustness. The implemented system must be able to plan a safe and efficient path for these different robots. To achieve this task, the TEA* (Time Enhanced A*) graph search algorithm will be used to coordinate the paths of the robots, along with a graph decomposition module that will be used to improve the efficiency and safety of this system. The project was implemented using the ROS framework and the Stage simulator. Results validate the proposed approach since the system was able to coordinate a fleet of robots in various different tests efficiently and safely, given the heterogeneity of the robots.

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The list of Adaptive Optics (AO) simulators in the community has constantly been growing, guided by different needs and purposes (Compass, HCIPY, OOMAO, SOAPY, YAO. . .). In this paper, we present OOPAO (Object Oriented Python Adaptive Optics), a simulation tool based on the Matlab distribution OOMAO to adapt its philosophy to the Python language. This code was initially intended for internal use but the choice was made to make it public as it can benefit the community since it is fully developed in Python. The OOPAO repository is available in free access on GitHub (https://github.com/cheritier/OOPAO) with several tutorials. The tool consists of a full end-to-end simulator designed for AO analysis purposes. The principle is that the light from a given light source can be propagated through multiple objects (Atmosphere, Telescope, Deformable Mirror, Wave-Front Sensors. . .) among which experimental features can be input, in the spirit of OOMAO. This paper provides an overview of the main capabilities of the code and can be used as a user manual for interested users.

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