The impact of Clean Spark Spread expectations on storage hydropower generation

Abstract Storage hydropower generation plays a crucial role in the electric power system and energy transition because it is most widespread with low greenhouse gas emissions and, moreover, relatively cheap to ramp up down. As result, provides flexibility grid helps mitigate short-term production uncertainty that affects green technologies. However, using water reservoirs represents an opportunity cost, which related evolution of plant capacity profitability. latter wide range types variables, order incorporate large-scale prediction model important select variables impact on storage generation. In this paper, we investigate influencing choices price maker producers, particular study Clean Spark Spread expectations hydroelectric connection, entropy machine learning tools, present method for embedding predict generation, showing that, some time horizon, CSS have greater than prices. It shown if right mix considered, error drastically reduced. This implies into model.