The European Green Deal aims to reduce net greenhouse gas emissions by at least 55% by 2030, compared to 1990 levels. Renewable energy policies must make an important contribution to achieving this challenging objective. In the ongoing revision of the Renewable Energy Directive (RED), it is proposed to increase the overall share of renewables to 40%.Buildings and transport will drive the energy transition; in both these end-use sectors, progressive electrification is expected:- in transport, with an increase in vehicle charging consumption and new opportunities for short-term grid balancing and storage services;- in buildings, with a growing role of heat pumps and new issues of coupling of the heat and electricity demand profiles.To face this strong increase in electricity demand, a high penetration of renewables is needed.Currently the generation and use of renewable electricity is based on two main configurations: i) large plants that feed into the grid; ii) small plants for the self-consumption of single users. Both approaches have structural limitations. The concentration of generation in large plants can lead to significant environmental and land consumption impacts. In addition, centralized generation processes can be inefficient with respect to the localization and the dynamics of energy demand. Distributed generation of individual users avoid the main part of impacts issues. Typically, the design criteria of small plants is optimized on the real energy needs of single users. But, from another point of view, the size of individual plants is often smaller than the onsite available installation capability. In this way, much of the renewable potential is untapped.Sharing energy is the key practice to overcome these limitations in and to enable further developments of renewable generation. According to definitions of RED, local energy communities are the main instrument to allow energy sharing and value exchange between different users. The concept of energy community is evolving rapidly: from small associations for photovoltaic generation and self-consumption to complex energy systems containing different energy sources in a wide territorial extension.The PhD research will focus on the development of simulation and optimization techniques to maximize energy, environmental and economic performance of energy communities. Multiplayer systems will be considered, in which different actors interact to optimize the exploitation of renewable sources such as energy communities and systems with energy storage of different forms (thermal, electrochemical, hydrogen, etc.). Data analysis techniques will be involved, such as clustering techniques and Machine Learning methods, such as linear regression, Support Vector Regression and artificial neural networks. The application of these techniques in the energy fields (energy demand prevision, user needs analysis etc.) has already provided good results in terms of accuracy and computational efficiency if compared to the techniques of more traditional simulations and has now reached a degree of development that can be applied in real energy systems. Therefore, this approach will be applied to the study of the design and operation of energy communities based on real case studies.

Good knowledge on: energy systems modelling, energy data analysis, energy generation and distribution, cogeneration, district heating, energy self-production at user scale (prosumer), renewable energy resources, technologies and supply chains (e.G. Wood biomass, solar thermal and photovoltaic, hydrothermal and aerothermal by heat pumps). 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 to 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.