Here we are concerned with an agent based monitoring of SGs with assessment of optimal settings obtained through approximate Optimal Power Flow (OPF) solutions. The primary goal of a generic OPF problem is to minimize the total production costs of the entire system to serve the load demand while maintaining the security of the system operation.The consideration behind the proposed approach is that large historical operation dataset are usually available in SGs and employed to extract useful information; besides, such datasets are also expected to grow over and over because of the pervasive deployment of SGs sensors. The proposed approach combines the MAS technology with the approximation properties of Fuzzy transform (F-Transform). F-transform is a fuzzy approximation technique, stating a functional dependency through a linear combination of basic functions. F-transforms are used in order to address two issues: first to reduce the storage need, by compressing the historical datasets, and second to provide agents with fast and reliable actions to get accurate OPF solutions, by a similarity search throughout the compressed historical dataset.

It should be pointed out that in the SG context, the large volume of the datasets makes data collection, storage and processing a very complex and demanding task. Hence, effective tools aimed at reducing the size and the cardinality of SGs data may be very beneficial. Besides, the solution of OPF problems in SG often requires the compliance with strictly time constraints. In such a context, an approximate solution, through fast computation, is often more useful than a high quality one, implying a higher computational cost. The proposed approach is based on an important property: F–transform preserves similarity. More precisely, under certain conditions, it can be proved that the minimum Euclidean distance in the transformed domain corresponds to the minimum Euclidean distance in the original domain. Then search operations in the transformed domain provide not only fast but also reliable results. The proposed method has been tested on small and large power networks.

The aim of this project is to study sedimentation of erythrocytes properties and to find the mathematical model that describes this process. Numerical simulations are also expected.

In this project we will study the collective motion of a crowd leaving an unknown area under limited visibility. In this scenario we have two main phases first exploration, second evacuation, and their interplay is strongly influenced by the "emotional state" of the crowd: from normal to panic situations. Thus we will concentrate on the design of safety strategies to ease evacuation time, using tools from optimal control theory. In particular we will explore the following issues: (i) leaders-followers dynamics, (ii) obstacle optimization, (iii) emergency signal positioning.

Numerical simulations will validate the proposed modelling framework, comparing different strategies efficiency in various settings.

Finally, we will calibrate our models using real market data from the European Energy Exchange (EEX) and study their performance against standard approaches implemented in MATLAB.

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