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The increased relevance of renewable energy sources has modified the behaviour of the electrical grid.

Some renewable energy sources affect the network in a distributed manner: whilst each unit has little influence, a large population can have a significant impact on the global network, particularly in the case of synchronised behaviour.

On the other hand, a more practical project will apply the above theoretical connections on a simple models setup in the area of robotics and autonomy.

Courses: Computer-Aided Formal Verification, Probabilistic Model Checking, Machine Learning This project shall investigate a rich research line, recently pursued by a few within the Department of CS, looking at the development of quantitative abstractions of Markovian models.

This enables the verification of MPL models against temporal specifications within the SPIN model checker.

Courses: Computer-Aided Formal Verification, Numerical Solution of Differential Equations Prerequisites: Some familiarity with dynamical systems, working knowledge of MATLAB and C Sensorisation and actuation in smart buildings and the development of smart HVAC (heat, ventilation and air-conditioning) control strategies for energy management allow for optimised energy usage, leading to the reduction in power consumption or to optimised demand/response strategies that are key in a rather volatile market.

This work investigates the behaviour of a large, heterogeneous population of photovoltaic panels connected to the grid.

LTS can be pictorially represented via the Graphviz tool and exported to PROMELA language.Analysis and simulations of the model show that it is a realistic abstraction, and quantitatively indicate that heterogeneity is necessary to enable the overall network to function in safe conditions and to avoid load shedding.This project will provide extensions of this recent research. Prerequisites: Computer-Aided Formal Verification, Probabilistic Model Checking Stochastic Hybrid Systems (SHS) are dynamical models that are employed to characterize the probabilistic evolution of systems with interleaved and interacting continuous and discrete components.Among other advantages, microgrids have shown positive effects over the reliability of distribution networks.These systems present heterogeneity and complexity coming from 1. the presence of nonlinear dynamics both over continuous and discrete variables.

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Abstractions come in the form of lumped, aggregated models, which are beneficial in being easier to simulate or to analyse.

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