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

We employ Markov models to represent the aggregated behaviour of the population, while the rest of the network (and its associated consumption) is modelled as a single equivalent generator, accounting for both inertia and frequency regulation.

These factors calls for the development of proper quantitative models.

This framework provides the opportunity of employing formal methods to verify properties of the microgrid.

Courses: Computer-Aided Formal Verification, Probabilistic Model Checking, Machine Learning Reinforcement Learning (RL) is a known architecture for synthesising policies for Markov Decision Processes (MDP).

We work on extending this paradigm to the synthesis of ‘safe policies’, or more general of policies such that a linear time property is satisfied.

The plan for this project is to make the first steps in this direction, based on recent results in the literature.

The project can benefit from a visit to Honeywell Labs (Prague). Prerequisites: Some familiarity with dynamical systems. This project will explore connections of techniques from machine learning with successful approaches from formal verification.

This project, grounded on existing literature, will pursue (depending on the student's interests) extensions of this recent work, or its implementation as a software tool.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.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.

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