In recent years, massive numbers of distributed energy resources are being installed in distribution feeders at both the utility and customer levels. These integrations are creating bi-directional power ow complexities in all the existing applications of distribution management system (DMS) and outage management system (OMS). Therefore, a significant interest is growing among utilities to combine all the DMS and OMS functionalities with distributed energy resources management system. This combined structure is known as advanced distribution management system (ADMS). ADMS is comprised of three major applications: conservation voltage reduction (CVR), demand response(DR), and fault location, isolation, and service restoration (FLISR). In this dissertation, some critical challenges for ADMS applications are defined with the proposed solutions. The objectives of this dissertation are: (1) Selection of appropriate load model for the assessment of CVR in a time-varying manner; (2) Implementation and assessment of CVR with the integration of DER and considering time varying stochasticity; (3) A combined framework of CVR and DR for maximizing the energy efficiency; (4) Fault location scheme for FLISR with very high penetration of DER.

The first objective of this dissertation is to select the most appropriate voltage sensitive composite load model. The selection of an appropriate load model can lead to the calculation of energy savings precisely with near accurate CVR factor. In addition, appropriate load model can help to calculate the voltage sensitivity factor more precisely which can help the utility to identify improvements in voltage profile throughout a distribution feeder during CVR deployment. Therefore, two widely used voltage sensitive load models- exponential and ZIP load model are extensively analyzed and compared. Two different dual-stage filtering techniques are discussed for retrieving load coefficients for individual models.

The second objective defines a model of CVR deployment where voltage sensitive loads and smart inverter interfaced DERs are considered for effective CVR planning. Voltage control strategies of smart inverters provide additional benefits for flattening the voltage profile while deploying CVR. In addition, a lower voltage set point at the substation helps to deploy deeper CVR deployment and more energy savings. Moreover, a time-varying stochastic CVR deployment is also shown for advanced planning which might help the utility to know about the most beneficial times if short-term CVR deployment is intended.

The third objective develops a framework to maximize the energy efficiency by integrating CVR and DR both. This framework considers the deepest CVR deployment possible, such as objective two, with the addition of load shifting based DR. In addition, DR program in this model considers different tariff/incentive plans and provides the most suitable one for each customer. The entire model also depicts an energy management plan for individual customers. The model considers the integration of PV based DERs and battery energy storage systems. Stochasticity in load consumption and DER injection is also considered.

The final objective of this dissertation is to demonstrate a fault location scheme for FLISR. The scheme is developed based on the collected data of wireless sensors and smart meters when faults occur. The scheme considers bi-directional power flows with high penetration of DERs. In addition, simultaneous occurrence of faults at different locations of a distribution feeder is considered in case of any natural calamity. The model is independent of building an impedance matrix. Instead, it uses a graph theory based method to narrow the search space of suspected faults. These objectives are beneficial for both utility and customers in terms of energy efficiency, reliability, and resiliency. All these objectives are developed theoretically and tested in utility scale distribution feeders. In addition, the future challenges are also described for the modernization of the envisioned distribution system.