This PhD thesis comprises three essays about renewable energy and energy markets. In the first chapter, I study the case of less developed countries that have large solar energy resources but struggle with low solar capacity deployment. The study subject is a region in Brazil that has a developed South, in contrast to an underdeveloped North. I develop a directed subsidy scheme that attempts to increase residential solar deployment in the North by leveraging superior solar irradiation. Results show that improvements are possible, but they are limited. In the second chapter, I analyze the effects of the renewable energy portfolio diversification that took place in Texas over the past ten years. Results indicate that the subsidy scheme adopted by the US federal government contributed to making aggregated renewable generation more volatile. Additionally, renewable portfolio diversification can increase CO2 emissions by facilitating the dispatch of coal power plants. In the third chapter, I propose a method to improve the load forecasting performed by neural networks. The method is inspired by bootstrapping and involves retraining copies of these neural networks and combining their forecasts.