The European Climate Act and the Fit for 55 EU directive have set increasingly challenging targets to reduce emissions and increase the development of renewable energy sources.To achieve these goals, distributed generation from renewable electricity will have the greatest prospect of growth, both to cover civil and industrial electricity needs, and as part of the electrification process of the transport sector, also through the development of energy communities.The 'Green Revolution and Ecological Transition' and 'Infrastructure for Sustainable Mobility' are in fact two of the six key themes of the PNRR, themes within which the following proposal fits to support the sustainable development and use of local resources. (Mission M2C2 - Renewable Energy, Hydrogen, Grid and Sustainable Mobility | Investment 1.2: Promoting Renewables for Energy Communities and Self-Consumption; Investment 2.1: Strengthening the Smart Grid. Mission M2C1.3 Develop integrated projects | Investment 3.2: Green communities).The doctoral research will focus on the development of simulation and optimisation techniques to maximise the energy, environmental and economic performance of local energy communities and prosumers, and hardware devices to support smart grids. Optimisation systems will be implemented based on deterministic techniques, such as Mixed-Integer Linear Programming systems, and stochastic methods such as artificial immune networks. In addition, the research will apply game-theoretic and agent-based model methods, where different actors interact to optimise the exploitation of renewable energy sources such as energy communities and energy storage systems.Data analysis techniques such as clustering techniques and machine learning methods (linear regression, support vector regression and artificial neural networks) will be involved. The application of these techniques in the energy sector (energy demand forecasting, user needs analysis, etc.) has already provided good results in terms of accuracy and computational efficiency compared to more traditional simulation techniques and has now reached a degree of development that can be applied to real energy systems.The hardware infrastructure and the software platforms for the implementation of the identified solutions will then be defined, also in the context of the current energy market and its evolution, and their application on pilot cases developed in collaboration with local energy operators.

Good knowledge on: energy systems modelling, energy data analysis, energy generation and distribution, energy self-production at user scale (prosumer), renewable energy resources. Base knowledge on: data processing, data analysis and spatial representation software (e.G. Excel, Matlab, R studio, GIS).

Politecnico di Torino carries out education, research, technological transfer, and services in all sectors of architecture and engineering. The Department of Energy is the point of reference in Politecnico di Torino for the areas of knowledge concerned with energy and sustainable development. The candidate will join the Sustainable Energy Analysis (SEA) and the Computer Aided Design of ElectroMagnetic Apparatuses (CADEMA) research groups that works on local energy planning and on optimization procedures to energy management and network systems.SEA research group is coordinated by prof. Alberto Poggio. Main research topics addressed concern: energy transition at urban and regional scale, energy analysis of industrial processes and cogeneration plants, sustainable supply chains for wood biomass energy, renewable heat for district heating. SEA team integrates a knowledge of energy technologies with multi-scale analysis, from the individual user or plant up to an entire territory, and a multidisciplinary approach, including issues related to climate change, air quality, territorial management, local development.CADEMA research group is coordinated by prof. Maurizio Repetto and groups professors and researchers mainly belonging to the scientific sector of “Principle of Electrical Engineering” with competences in simulation and optimization of complex system by means of evolutionary and neural computation. Analysis tools have been developed for hybrid energy systems and for multi-agent sharing structures as the one of energy community.