Abstract Decarbonisation policies aim at reducing fossil fuel based generation in favour of cleaner renewable energy sources. Changes in the generation mix to supply future electricity demand will require tools capable to emulate the bidding behaviour of new generation plants. Price forecasting tools lacking this feature and only based on historical data time series might soon become not satisfactory for this scope. This paper presents a methodology that, by considering hourly electricity generation offers (price, volumes) datasets, allows simulating future electricity wholesale's prices. This is done by taking into account new generation units and the dismissing of old (coal-based) units according to the demand and generation forecasts in the European Ten Year Network Development Plan (TYNDP) 2030 scenarios. Machine learning, clustering and distribution sampling techniques are used in this work to finally estimate prices distribution in 2030 in the biggest bidding zone of the Italian market. The results suggest that the prices obtained in the different scenarios do converge to those estimated by the TYNDP. The approach used bypasses the need to have access to all the transactions of a given market. Probability distributions are in fact enough in the proposed methodology to achieve similar results to those based on full knowledge of transaction datasets.