Abstract
This paper proposes a framework for the optimal operation of multi Micro Grids (multiMGs) based on Hybrid Stochastic/Robust optimization. MultiMGs with various characteristics are considered in this study. They are connected to different buses of their Up-Stream-Network (USN). Day-Ahead (DA) and Real-Time (RT) markets are contemplated. The proposed optimization structure in this paper is a bi-level one since both MGs operators' and USN operator's decisions are considered in the proposed model. The advantages of using time-of-use demand response programs on the optimal operation of USN in the presence of multiMGs are investigated. The uncertainty of different components, including wind units, photovoltaic units, plug-in electric vehicles, and DA market price is captured by using stochastic programming. In addition, robust programming is utilized for contemplating the uncertainty of the RT market price. Furthermore, the grid-connected and island modes of MGs' operation are investigated in this paper, discussing also the virtues of utilizing multiMGs over single MG. Finally, IEEE 18-bus and 30-bus test systems are considered for MGs and USN networks respectively to scrutinize the simulation results.

Abstract
Learning from data streams can only be realized by systems which are not only effective but also efficient. That is, knowledge discovery in this context is impossible without being aware of the computational resources available. Weightless artificial neural networks (WANNs) are based on an alternative principle to iterative optimization of weights employed by most mainstream artificial neural network models and related tools. WANNs explicitly manage knowledge pieces, which are stored by RAM nodes. Such foundational difference reflects on the adaptability of these models to streaming inputs: in such scenario, the application of weightless models can be considered more natural than the same for their weighted counterparts, with an ample control over learning capability as well as resources consumption. This chapter details a WANN-based approach for mining data streams, which allows the maintenance of an up-to-date data summary which can be used for several purposes. The insights and original ideas which power such model are explained as well, enabling novel applications and further development of them.

Abstract

Abstract

Abstract
This chapter discusses the development of improved citizen services taking into consideration integration of agent-based modelling and simulation experience into conceiving, design and implementation activities with a strong focus on technology enabled service systems. Service design is formalized here towards the integration of customer experience, validated through service interaction modelling. Integration of user experience at design stage in the value co-creation process is a possible immediate evolution direction of projects in the Smarter Cities perspective. Guidelines for integrating a modelling and simulation perspective in service design are presented along with the Socio-Technical Systems Engineering process. The case study presented here is dedicated to Smart Transport. The chapter opens a larger discussion on specific research directions and knowledge transfer related to Smart Transport as highlighted in EU projects.

Abstract
Knowledge of the atmospheric turbulence in the telescope line-of-sight is crucial for widefield observations assisted by adaptive optics (AO), particularly tomodel how the point spread function (PSF) elongates across the field of view(FOV) owing to the anisoplanatism effect. The extraction of key astronomical parameters accounts on an accurate representation of the PSF, which call for an accurate anisoplanatism characterisation . This one is, however, a function of the Cn2(h) profile, which is not directly accessible from single-conjugate AO telemetry. It is possible to rely on external profilers, but recent studies have highlighted discrepancies of more than 10 per cent with AO internal measurements, while we aim at better than 1 per cent accuracy for PSF modelling. In order to tackle this limitation, we present focal-plane profiling (FPP) as a Cn2(h) profiling method that relies on post-AO focal-plane images.We demonstrate that such an approach complies with a 1 per cent level of accuracy on the Cn2(h) estimation and establish how this accuracy varies regarding the calibration star magnitudes and their positions in the field. We highlight the fact that photometry and astrometry errors caused by PSF mis-modelling reach respectively 1 per cent and 50 µas using FPP on a Keck baseline, with a preliminary calibration using a star of magnitude H = 14 at 20 arcsec. We validate this concept using Canada's NRC-Herzberg HeNOS testbed images by comparing FPP retrieval with alternative Cn2 (h) measurements on HeNOS. The FPP approach allows the Cn2(h) to be profiled using the SCAO systems and significantly improves the PSF characterization. Such a methodology is also ELT-size-compliant and will be extrapolated to tomographic systems in the near future.