Some contributions on Energy Finance: the new challenge of renewable energy production.

The expansion of renewable energy sources is destined to increase and requires the development of theoretical models to correctly estimate both the production of electricity and its economic convenience. We consider the Italian futures traded on the IDEX market, examining the possible interrelation between futures and spot prices, drawing on the ex post risk premium and the net convenience yield. To understand this process we analyze the variables that influence these parameters through different regression techniques obtaining promising results. Innovative aspects of the work are: the application to a market not yet explored from this point of view, the improvement of the regression techniques best suited to the phenomenon in question and the use of a wider set of explanatory variables. The issue of renewable energy is addressed starting from wind energy and the consideration that those who want to invest in the construction of a wind farm are faced with a sector full of uncertainties, mainly linked to variations in wind intensity and the price of electric energy. The expected gain generated by a wind turbine is given by three stochastic variables: wind intensity, energy price and quantity of energy produced, all measured on an hourly basis. Once these three variables have been modeled, it is possible to determine profit as the sum of the discounted values of the product between the price of energy and the amount of energy produced each hour. A new stochastic model is proposed to simulate the expected income of a wind turbine, a multivariate model for wind speed and the price of electricity. Through Monte Carlo simulations (MC) it is verified that the model obtained approximates the expected income better than the one that simulates both marginals independently. We then move on to the analysis of a photovoltaic system through the modeling of the stochastic factors that determine it: quantity of incident solar radiation and some climatic variables that modify the efficiency of the solar panels (temperature and wind speed). Finally, to evaluate the income deriving from the production of electricity, we applied a solar radiation/electricity price bivariate model to address the correlation between these two variables, noting how the simulated values faithfully replicate the empirical ones. The VAR models used to estimate photovoltaic production and the price of electricity have proved to be sufficiently flexible and applicable to different climatic conditions. A fundamental aspect of our model is having considered the observed dependence structure between the price of electricity and solar radiation. This particularity is also valid outside the local context used for our numerical simulations so we can hypothesize that the type of model used here may cover a wider field of application. Finally, we have developed options as to address both volumetric and price risks. These options were then priced with the MC method. We conclude with the analysis of hybrid plants that exploit different climatic sources to generate electricity. To obtain a reliable estimate of the total energy produced and the expected income, we apply a multivariate model that links the climatic variables (solar radiation and wind speed) and the price of electricity, allowing us to reproduce the dependency structure with the same opportunity. between these variables. We validated the model by verifying that the results obtained are better than the situation in which the individual stochastic variables are modeled independently. In addition, we apply Markowitz's portfolio selection theory to determine the optimal mix that maximizes the overall income of our portfolio. We also calculate the loss of load hours (LoLH) for efficient portfolios. Finally, we manage volumetric/market risk with quantum options and make an optimal choice between the two technologies. Models are validated through MC simulations using real data.