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In this article, energy expert Donato Leo discusses the use of deep learning and machine learning techniques to analyze and optimize energy market scenarios. Specifically, Leo uses these techniques to simulate the impact of utility-scale batteries on the PUN (National Single Price) curve, which represents the wholesale reference price of electricity in Italy. The results show that BESSs can decrease the maximum electricity price, increase the minimum price, and have a season-dependent effect on the average price. This has potential to increase earnings for PV owners, but it depends on the context and adoption of BESSs in the generation fleet.
