Abstract
Storage hydropower generation plays a crucial role in the electric power system and energy transition because it is the most widespread power generation with low greenhouse gas emissions and, moreover, it is relatively cheap to ramp up and down. As a result, it provides flexibility to the grid and helps mitigate the short-term production uncertainty that affects most green energy technologies. However, using water in reservoirs represents an opportunity cost, which is related to the evolution of plant production capacity and production profitability. As the latter is related to a wide range of types of variables, in order to incorporate it in a large-scale prediction model it is important to select the variables that impact most on storage hydropower generation. In this paper, we investigate the impact of the variables influencing the choices of price maker producers, and, in particular we study the impact of Clean Spark Spread expectations on storage hydroelectric generation. In this connection, using entropy and machine learning tools, we present a method for embedding this expectations in a model to predict storage hydropower generation, showing that, for some time horizon, expectations on CSS have a greater impact than expectations on power prices. It is shown that, if the right mix of power price and CSS expectations is considered, the prediction error of the model is drastically reduced. This implies that it is important to incorporate CSS expectations into the storage hydropower model.
Highlights
In the last decades the problem of reducing green-house gases emissions has obtained increasing worldwide concern and energy policies have been oriented towards a transformation of the global energy sector from fossil-based to zero-carbon sources, undertaking the so-called Energy Transition.In this scenario, hydropower is of paramount importance as it is the most widespread source of electricity with low GHG emissions
Hydropower is of paramount importance as it is the most widespread source of electricity with low GHG emissions. It generates about 60% of renewable electricity and has median life-cycle carbon equivalent intensity of 18.5 gCO2-eq/kWh (IHA 2018). Those equipped with storage technology play a crucial role for the electric power system and for the energy transition
The aim of this paper is to study the impact of Clean Spark Spread expectations on the aggregate monthly hydroelectric generation and to provide a method to embed this expectations in a storage hydropower generation model
Summary
In the last decades the problem of reducing green-house gases emissions has obtained increasing worldwide concern and energy policies have been oriented towards a transformation of the global energy sector from fossil-based to zero-carbon sources, undertaking the so-called Energy Transition In this scenario, hydropower is of paramount importance as it is the most widespread source of electricity with low GHG emissions. Hydropower is of paramount importance as it is the most widespread source of electricity with low GHG emissions It generates about 60% of renewable electricity and has median life-cycle carbon equivalent intensity of 18.5 gCO2-eq/kWh (IHA 2018). Among hydropower plants, those equipped with storage technology play a crucial role for the electric power system and for the energy transition. Storage hydropower plants offer flexibility to the grid and help mitigate the short-term production uncertainty that affects most green energy technologies, as shown in Albadi and El-Saadany (2010) and Hirth (2016) for wind and in Komiyama and Fujii (2014) for solar power
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