Guidelines for minimum cost transition planning to a 100% renewable multi-regional energy system

Piero Danieli, Gianluca Carraro, Gabriele Volpato, Enrico Dal Cin, Andrea Lazzaretto and Massimo Masi

Applied Energy, 2024, vol. 357, issue C, No S0306261923018615

Abstract: The paper deals with the energy transition, focusing on the decisions to be taken about the change in the energy conversion capacity of specified portion of the universe, such as industrial or domestic districts, regions, countries, etc. Conversion capacity is intended here as the set of all energy conversion and storage units characterized by type, size and number, and interacting with the natural gas and electricity grids. The goal is to find guidelines for new installations, obtained by simulations of a model able to describe properly the behavior of each energy conversion technology and the interaction with grids. The model is based on a traditional MILNP approach, but contains unique features in the literature, which allow to obtain results of general validity for applications to very different geographical areas, or to countries including very different regions that transmit energy with each other or export/import it from abroad. The main novelty of this work is to identify the best planning for the decarbonisation of energy demand sectors, according to the criterion of minimum cost, both at global and at regional level. The results applied to the case of Italy allow identifying clearly the energy demand sectors to which new installations should be applied first to minimize the cost for the total energy system, and the units that must be foreseen to satisfy these energy demands, i.e. a precise strategy for the energy transition towards a 100% renewable system.

Keywords: Energy transition path; MINLP modeling; Multi-regional energy system; Power-to-gas; Decarbonization of transport sector; Sustainable energy systems (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (4)

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DOI: 10.1016/j.apenergy.2023.122497

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