Global energy consumption and the related carbon dioxide emissions, which represent a large share of the overall anthropogenic greenhouse gas production, are continuously increasing since most of the energy needs are still provided by fossil fuels, thus constituting one of the main issues to be addressed in the climate change mitigation agenda. To achieve the Paris Agreement’s ambitious objectives, an energy transition towards sustainable energy systems based on the new smart energy system (SES) paradigm is needed, thus integrating the various energy sources, vectors and needs within the sectors (electricity, heating, cooling, transport, etc.).
However, optimal planning, design and management of complex integrated systems such as SES require to make use of proper decision support models based on multi-objective optimization techniques, since a sustainability analysis intrinsically involves environmental, economic and social aspects. Furthermore, a SES project involves several stakeholders, each driven by different and often conflicting objectives, which should be considered within such models, to remove some relevant barriers to the energy transition.
Focusing on the improvement of the sustainability of the energy-intensive sectors, the main objective of this thesis is thus the development of a decision support framework based on multi-objective optimization with the aim to support the decision makers in the planning, design and management of integrated smart energy systems, while considering the different involved stakeholders. The proposed model, composed by three main phases (namely investigative, design and decision-making), has been developed by steps via its application on case studies belonging to two main topics concerning the improvement of the sustainability performance of energy-intensive sectors through the implementation of the smart energy system concept. The first main topic is representative of the context of industrial districts and concerns their sustainable energy supply based on technical solutions specifically designed for cluster of firms, allowed by geographical proximity. The other one concerns the synergic integration between industrial and urban areas, through the recovery of waste energy from industrial processes to feed municipal district heating with a carbon-free source. The case studies have been selected, within the opportunities available in the local territorial context, not only because fit for the implementation of the smart energy system concept, but also due to their suitability for the implementation of different phases of the proposed decision support system (DSS).
