This research identifies several barriers to large scale implementation of solar photovoltaics into the modern US electricity system, along with solutions to help mitigate these challenges. The need for new technologies and utility rate plans are identified as two of these key barriers. In place of expensive, developing technologies this research explores the use of thermal energy storage (TES), a widely used, inexpensive, mature technology as a potential solution for a portion of this problem. A real-life example from Arizona State University (ASU) is used to illustrate the potential of TES. In addition, shortcomings of modern electricity rate plans are identified using both cost and system characteristics of residential solar and battery systems. This rate and system modeling also gives insight into the value that solar can provide to residential customers in a variety of settings.

Accurate forecasting of electricity prices has been a key factor for bidding strategies in the electricity markets. The increase in renewable generation due to large scale PV and wind deployment in California has led to an increase in day-ahead and real-time price volatility. This has also led to prices going negative due to the supply-demand imbalance caused by excess renewable generation during instances of low demand. This research focuses on applying machine learning models to analyze the impact of renewable generation on the hourly locational marginal prices (LMPs) for California Independent System Operator (CAISO). Historical data involving the load, renewable generation from solar and wind, fuel prices, aggregated generation outages is extracted and collected together in a dataset and used as features to train different machine learning models. Tree- based machine learning models such as Extra Trees, Gradient Boost, Extreme Gradient Boost (XGBoost) as well as models based on neural networks such as Long short term memory networks (LSTMs) are implemented for price forecasting. The focus is to capture the best relation between the features and the target LMP variable and determine the weight of every feature in determining the price.

The impact of renewable generation on LMP forecasting is determined for several different days in 2018. It is seen that the prices are impacted significantly by solar and wind generation and it ranks second in terms of impact after the electric load. The results of this research propose a method to evaluate the impact of several parameters on the day-ahead price forecast and would be useful for the grid operators to evaluate the parameters that could significantly impact the day-ahead price prediction and which parameters with low impact could be ignored to avoid an error in the forecast.