Extending the land use sector representation in open source bottom-up energy system optimization models: The TEMOA-Pantelleria case study

In the context of the fight against climate change, energy system optimization models are becoming increasingly useful for this purpose. These models enable us to represent and analyse environmentally impactful sectors, such as the energy and agricultural ones. Currently, numerous sector-specific models are available for in-depth impact assessment. However, these models frequently lack a holistic perspective, neglecting to account for the interconnections between the sector under analysis and other related sectors, e.g. water and land use. In response to these issues, a recent approach called the Water Energy Food (WEF) Nexus has gained growing relevance. This approach emphasizes the significance of adopting a comprehensive perspective in managing and optimizing the intricate interconnections among water, energy, and food systems. In the framework of the Nexus, Integrated Assessment Models (IAMs) link multiple sectors and consider the synergies between them, understanding and analysing the potential consequences of different scenarios. In this context, islands, especially isolated ones, can serve as unique Nexus laboratories due to their limited natural resources and smaller spatial scale. In this thesis, to investigate possible trade-offs and synergies between the energy and the land use sectors, a model of the latter has been added to an energy system optimization model and applied to a well-defined case study. The versatile, open-source energy system optimisation model, TEMOA (developed in Python), has been selected, for which an instance was already developed for the Pantelleria Island in Italy. Crop yields from FAOSTAT (the best European statistics site by FAO) and ISTAT (Italy's primary statistics source) have been considered, together with several factors for crop modelling (energy usage for fertilization, machinery, and irrigation). As for the land, data on elevation, slope, irradiance, wind productivity, and the distance from the electrical grid were collected for each spatial parcel. Advanced clustering methods were tested and eventually used to incorporate the land use data into the model. To represent the land use sector within the TEMOA framework, new parameters such as Land Use Intensity, land area, and crop fuel consumption have been added to the model. Finally, after the implementation of the land-use sector. The model was able to optimise the use of soil choosing between different crops and RES to be installed on the spatial domain under investigation. The model was checked to be able to choose the technology that mostly reduces the economic output, expressed as the total cost of the energy-land system.