Abstract
The scope of energy system modelling is to support policy-makers in the definition of an energy strategy. Energy system models typically provide one single optimal solution. On the contrary, presenting the results of energy system modelling in the form of a set of optimal or sub-optimal alternatives improves the transparency towards the policy makers. A method to achieve this is marginal abatement cost curve. It estimates the relationship between potential reduction of CO2 emissions and relative costs. Model based methods to obtain marginal abatement cost curve lack of simultaneous high resolution in time and in sector coupling. Moreover, model based methods obtain smooth curves which can be transformed in step-wise only through a decomposition analysis. This latter shape is particularly important for providing the explicit technological detail in the graphical representation. The paper aims at developing a method to address these two issues in marginal abatement cost curves. The method, called EPLANoptMAC, is based on the EnergyPLAN software, developed by Aalborg university, and a hill climbing algorithm for expansion capacity optimisation. It is presented by applying it to the Italian energy system in 2030. The results show how in the initial phase of the decarbonisation process it is cheaper to generate overgeneration and curtailments from variable renewable energy sources than save these curtailments through balancing and storage solutions. This is driven by the low cost of generation of VRES and the high cost of balancing and storage solutions.