As humanity pushes for a more sustainable future, we will have an increased dependence on renewable energies which can be unreliable sources of power. Therefore, along with an increase in renewable energy sources we must also grow our energy storage capacity to mitigate the risks posed by unreliable energy generation. The purpose of this report is to weigh the benefits and drawbacks associated with multiple promising technologies that could be key to humanity’s future. To compare each technology, they will be scoring each system based on multiple factors, then compiling the data into a decision matrix to concisely present the findings. First, a system’s efficiency, or the amount of power that is returned compared to the total invested. Second, the scalability of each technology, which would include what materials are needed for a certain system and how they are procured. Finally, the cost of a system must be considered. The five storage methods covered are Compressed Air Energy Storage (CAES), Pumped Hydropower Storage (PHS), Lithium-Ion Batteries, Thermal Energy Storage (TES), and Flywheels.

In this dissertation, a distribution system operator (DSO) framework is proposed to optimally coordinate distributed energy resources (DER) aggregators' comprehensive participation in the retail energy market as well as wholesale energy and regulation markets. Various types of DER aggregators, including energy storage aggregators (ESAGs), dispatchable distributed generation aggregators (DDGAGs), electric vehicles charging stations (EVCSs), and demand response aggregators (DRAGs), are modeled in the proposed DSO framework. An important characteristic of a DSO is being capable of handling uncertainties in the system operation. An appropriate method for a market operator to cover uncertainties is using two-stage stochastic programming. To handle comprehensive retail and wholesale markets participation of distributed energy resource (DER) aggregators under uncertainty, a two-stage stochastic programming model for the DSO is proposed. To handle unbalanced distribution grids with single-phase aggregators, A DSO framework is proposed for unbalanced distribution networks based on a linearized unbalanced power flow which coordinates with wholesale market clearing process and ensures the DSO's non-profit characteristic. When proposing a DSO, coordination with the ISO is important. A framework is proposed to coordinate the operation of the independent system operator (ISO) and distribution system operator (DSO). The framework is compatible with current practice of the U.S. wholesale market to enable massive distributed energy resources (DERs) to participate in the wholesale market. The DSO builds a bid-in cost function to be submitted to the ISO market through parametric programming. A pricing problem for the DSO is proposed. In pricing problem, after ISO clears the wholesale market, the locational marginal price (LMP) of the ISO-DSO coupling substation is determined, the DSO utilizes this price to solve the DSO pricing problem. The DSO pricing problem determines the distribution LMP (D-LMP) in the distribution system and calculates the payment to each aggregator. An efficient algorithm is proposed to solve the ISO-DSO coordination parametric programming problem. Notably, our proposed algorithm significantly improves the computational efficiency of solving the parametric programming DSO problem which is computationally intensive. Various case studies are performed to analyze the market outcome of the proposed DSO framework and coordination with the ISO.