The ever-increasing need for energy coupled with environment concerns due to emissions from fossil fuel-based sources have created a major dilemma for experts searching for a solution. The adoption of Distributed Energy Resources (DERs) combining renewable sources such as solar, wind, and distributed storages into the grid along with energy efficiency of large consumers like commercial buildings have the potential to resolve this. Large deployment of commercial building microgrids integrated with DERs can achieve both. But high installation, operation and maintenance (O&M) costs as well as unexpected grid operational challenges emerging from them are preventing the large-scale real-world adoption and implementation. In this dissertation solutions to three aspects of these problems are offered.First, cost optimization of commercial building microgrids with DERs is implemented from a utility tariff perspective. This effort includes developing a universal optimization framework that addresses the diversity and complexity of tariffs implemented by utilities. Changes in tariffs are also analyzed so that users can determine the best possible scenario for them. A novel dynamic tariff is proposed that can help the utilities deal with the “Duck Curve” problem that causes grid operation challenge due to high solar penetration. Next, a novel data-driven approach is proposed for predictive maintenance of Battery Energy Storage System (BESS), an important DER component in a commercial building microgrid. This approach uses readily available electrical and thermal property data of the BESS and applies the proposed statistical analysis to identify the bad cells in the BESS. Performance comparison of different machine learning algorithms are also compared along with the application of non-conventional features for better state of charge (SOC) estimation results. Finally, a methodology is developed for sustaining a commercial building microgrid in an islanded condition through the incorporation of several DERs including Vehicle to Grid (V2G) operation of Electric Vehicles. Results from microgrid islanding operation were analyzed and validated against several outage scenarios relevant to California grid to demonstrate the effectiveness of the developed method.

Goals for sustainable transportation have encouraged people to adopt more Plug-in Electric Vehicles (PEVs). Incentives and other friendly policies are also facilitating additional PEV penetration. The increasing penetration of PEVs has put up new challenges and opportunities in both transportation and electric sector. Firstly, an optimal framework is introduced to find out the best PEV charging/discharging strategy for a microgrid in this dissertation that can aid the Distribution System Operator (DSO) in PEV Charging Station (CS) selection. Then, a comprehensive techno-economic analysis is executed for a combined Light Duty Electric Vehicle (LDEV) and Heavy Duty Electric Vehicle (HDEV) implementation. The optimal participation of PEV Vehicle to Grid (V2G) is explored later on in a critical demand response event. Finally, the load modeling aspect of PEV is accomplished and their impacts are presented based on the variety of building load, feeder types, PEV user behavior and co-simulation for both present and future scenarios. A real-time V2G is implemented in both grid-connected and off-grid mode to show the opportunities created by the PEV integration into the grid.