As inverter-based distributed energy resources (DERs) such as photovoltaic (PV) and battery energy storage system (BESS) penetrate within the distribution system. New challenges regarding how to utilize these devices to improve power quality arises. Before, PV systems were required to disconnect from the grid during a large disturbance, but now smart inverters are required to have dynamically controlled functions that allows them to remain connected to the grid. Monitoring power flow at the point of common coupling is one of the many functions the controller should perform. Smart inverters can inject active power to pick up critical load or inject reactive power to regulate voltage within the electric grid. In this context, this thesis focuses on a high level and local control design that incorporates DERs. Different controllers are implemented to stabilize the microgrid in an Islanding and resiliency mode. The microgrid can be used as a resiliency source when the distribution is unavailable. An average model in the D-Q frame is calculated to analyze the inherent dynamics of the current controller for the point of common coupling (PCC). The space vector approach is applied to design the voltage and frequency controller. Secondly, using inverters for Volt/VAR control (VVC) can provide a faster response for voltage regulation than traditional voltage regulation devices. Another objective of this research is to demonstrate how smart inverters and capacitor banks in the system can be used to eliminate the voltage deviation. A mixed-integer quadratic problem (MIQP) is formulated to determine the amount of reactive power that should be injected or absorbed at the appropriate nodes by inverter. The Big M method is used to address the nonconvex problem. This contribution can be used by distribution operators to minimize the voltage deviation in the system. / Master of Science / Reliable power supply from the electric grid is an essential part of modern life. This critical infrastructure can be vulnerable to cascading failures or natural disasters. A solution to improve power systems resilience can be through microgrids. A microgrid is a small network of interconnected loads and distributed energy resources (DERs) such as microturbines, wind power, solar power, or traditional internal combustion engines. A microgrid can operate being connected or disconnected from the grid. This research emphases on the potentially use of a Microgrid as a resiliency source during grid restoration to pick up critical load. In this research, controllers are designed to pick up critical loads (i.e hospitals, street lights and military bases) from the distribution system in case the electric grid is unavailable. This case study includes the design of a Microgrid and it is being tested for its feasibility in an actual integration with the electric grid. Once the grid is restored the synchronization between the microgrid and electric must be conducted. Synchronization is a crucial task. An abnormal synchronization can cause a disturbance in the system, damage equipment, and overall lead to additional system outages. This thesis develops various controllers to conduct proper synchronization. Interconnecting inverter-based distributed energy resources (DERs) such as photovoltaic and battery storage within the distribution system can use the electronic devices to improve power quality. This research focuses on using these devices to improve the voltage profile within the distribution system and the frequency within the Microgrid.

With the advent of distributed energy resources (DERs), electric vehicles, and demand-response programs, grid operators are in dire need of new monitoring and design tools that help improve efficiency, reliability, and stability of modern power networks. To this end, the work in this thesis explores a generalized modeling and analysis framework for two pertinent tasks: i) learning loads via grid probing, and; ii) optimizing power grid topologies for stability. Distribution grids currently lack comprehensive real-time metering. Nevertheless, grid operators require precise knowledge of loads and renewable generation to accomplish any feeder optimization task. At the same time, new grid technologies, such as solar panels and energy storage units are interfaced via inverters with advanced sensing and actuation capabilities. In this context, we first put forth the idea of engaging power electronics to probe an electric grid and record its voltage response at actuated and metered buses to infer non-metered loads. Probing can be accomplished by commanding inverters to momentarily perturb their power injections. Multiple probing actions can be induced within a few tens of seconds. Load inference via grid probing is formulated as an implicit nonlinear system identification task, which is shown to be topologically observable under certain conditions. The analysis holds for single- and multi-phase grids, radial or meshed, and applies to phasor or magnitude-only voltage data. Using probing to learn non-constant-power loads is also analyzed as a special case. Once a probing setup is deemed topologically observable, a methodology for designing probing injections abiding by inverter and network constraints to improve load estimates is provided. The probing task under noisy phasor and non-phasor data is tackled using a semidefinite-program relaxation. As a second contribution, we also study the effect of topology on the linear time-invariant dynamics of power networks. For a variety of stability metrics, a unified framework based on the H2-norm of the system is presented. The proposed framework assesses the robustness of power grids to small disturbances and is used to study the optimal placement of new lines on existing networks as well as the design of radial topologies for new networks. / Doctor of Philosophy / Increased penetration of distributed energy resources such as solar panels, wind farms, and energy storage systems is forcing utilities to rethink how they design and operate their power networks. To ensure efficient and reliable operation of distribution networks and to perform any grid-wide optimization or dispatch tasks, the system operator needs to precisely know the net load (energy output) of every customer. However, due to the sheer extent of distribution networks (millions of customers) and low investment interest in the past, distribution grids have limited metering infrastructure. Nevertheless, data from grid sensors comprised of voltage and load measurements are readily available from a subset of customers at high temporal resolution. In addition, the smart inverters found in solar panels, energy storage units, and electric vehicles can be controlled within microseconds. The work in this thesis explores how the proliferation of grid sensors together with the controllability of smart inverters can be leveraged for inferring the non-metered loads i.e., energy output of customers that are not equipped with smart inverters/sensors. In addition to the load learning task, this thesis also presents a modeling and analysis framework to study the optimal design of topologies (how customers are electrically inter-connected) for improving stability of our power networks.

Power generation from solar energy has significantly increased, and the growth is projected to continue in the foreseeable future. The main challenge of dealing with solar energy is its intermittent nature. The received irradiation energy of the sun on the earth's surface can fluctuate in a matter of seconds and cause voltage issues to power systems. Considering the high growth rate of solar photovoltaic (PV) resources, it is essential to be prepared to encounter and manage their high penetration levels. Currently, simplified approaches are used to model the impacts of cloud shadows on power systems. Using outdated standards also limits the penetration levels more than required. Approximately 40% of the new PV installations are residential, or installed at a low voltage level. Currently, all components between utility distribution transformers and customers/loads are either ignored or modeled with oversimplification. Furthermore, large PV systems require a considerable amount of land. However, point sensor models are currently used to simulate those systems. With a point model, the irradiance values measured at a point sensor are used to represent the output of a large PV system. However, in reality, clouds cover photovoltaic resources gradually and if the solar arrays are widespread over a large geospatial area, it takes some time for clouds to pass over the solar arrays. Finally, before 2014, participation of small-scale renewable resources was not allowed in controlling voltage. However, they can contribute significantly in voltage regulation. The main objective of this dissertation is to address the abovementioned issues in order to increase the penetration levels as well as precisely identify and locate voltage problems. A time-series analysis approach is used in modeling cloud motion. Using the time-series approach, changes of the received irradiation energy of the sun due to cloud shadows are simulated realistically with a Cloud Motion Simulator. Moreover, the use of the time-series approach allows implementation of new grid codes and standards, which is not possible using the old step change methods of simulating cloud impacts. Furthermore, all electrical components between utility transformers and customers are modeled to eliminate the inaccuracy due to using oversimplified models. Distributed PV models are also developed and used to represent large photovoltaic systems. In addition, the effectiveness of more distributed voltage control schemes compared to the traditional voltage control configurations is investigated. Inverters connect renewable energy resources to the power grid and they may use different control strategies to control voltage. Different control strategies are also compared with the current practice to investigate voltage control performance under irradiation variations. This dissertation presents a comprehensive approach to study impacts of solar PV resources. Moreover, simulation results show that by using time-series analysis and new grid codes, as well as employing distributed PV models, penetration of solar PV resources can increase significantly with no unacceptable voltage effects. It is also demonstrated that detailed secondary models are required to accurately identify locations with voltage problems. / PHD / Power generation from solar energy has significantly increased, and the growth is projected to continue in the foreseeable future. The main challenge of dealing with solar energy is its intermittent nature. The received irradiation energy of the sun on the earth’s surface can fluctuate in a matter of seconds and cause voltage issues to power systems. Considering the high growth rate of solar photovoltaic (PV) resources, it is essential to be prepared to encounter and manage their high penetration levels. Currently, simplified approaches are used to model the impacts of cloud shadows on power systems. Using outdated standards also limits the penetration levels more than required. Approximately 40% of the new PV installations are residential, or installed at a low voltage level. Currently, all components between utility distribution transformers and customers/loads are either ignored or modeled with oversimplification. Furthermore, large PV systems require a considerable amount of land. However, point sensor models are currently used to simulate those systems. With a point model, the irradiance values measured at a point sensor are used to represent the output of a large PV system. However, in reality, clouds cover photovoltaic resources gradually and if the solar arrays are widespread over a large geospatial area, it takes some time for clouds to pass over the solar arrays. Finally, before 2014, participation of small-scale renewable resources was not allowed in controlling voltage. However, they can contribute significantly in voltage regulation. The main objective of this dissertation is to address the above mentioned issues in order to increase the penetration levels as well as precisely identify and locate voltage problems. A time-series analysis approach is used in modeling cloud motion. Using the time-series approach, changes of the received irradiation energy of the sun due to cloud shadows are simulated realistically with a Cloud Motion Simulator. Moreover, the use of the time-series approach allows implementation of new grid codes and standards, which is not possible using the old step change methods of simulating cloud impacts. Furthermore, all electrical components between utility transformers and customers are modeled to eliminate the inaccuracy due to using oversimplified models. Distributed PV models are also developed and used to represent large photovoltaic systems. In addition, the effectiveness of more distributed voltage control schemes compared to the traditional voltage control configurations is investigated. Inverters connect renewable energy resources to the power grid and they may use different control strategies to control voltage. Different control strategies are also compared with the current practice to investigate voltage control performance under irradiation variations. This dissertation presents a comprehensive approach to study impacts of solar PV resources. Moreover, simulation results show that by using time-series analysis and new grid codes, as well as employing distributed PV models, penetration of solar PV resources can increase significantly with no unacceptable voltage effects. It is also demonstrated that detailed secondary models are required to accurately identify locations with voltage problems.

Due to increasing penetration of solar power generation, distribution grids are facing a number of challenges. Frequent reverse active power flows can result in rapid fluctuations in voltage magnitudes. However, with the revised IEEE 1547 standard, smart inverters can actively control their reactive power injection to minimize voltage deviations and power losses in the grid. Reactive power control and globally optimal inverter coordination in real-time is computationally and communication-wise demanding, whereas the local Volt-VAR or Watt-VAR control rules are subpar for enhanced grid services. This thesis uses machine learning tools and poses reactive power control as a kernel-based regression task to learn policies and evaluate the reactive power injections in real-time. This novel approach performs inverter coordination through non-linear control policies centrally designed by the operator on a slower timescale using anticipated scenarios for load and generation. In real-time, the inverters feed locally and/or globally collected grid data to the customized control rules. The developed models are highly adjustable to the available computation and communication resources. The developed control scheme is tested on the IEEE 123-bus system and is seen to efficiently minimize losses and regulate voltage within the permissible limits. / Master of Science / The increasing integration of solar photovoltaic (PV) systems poses both opportunities and technical challenges for the electrical distribution grid. Although PV systems provide more power to the grid but, can also lead to problems in the operation of the grid like overvoltages and voltage fluctuations. These variations can lead to overheating and burning of electrical devices and equipment malfunction. Since the solar generation is highly dependent on weather and geographical location, they are uncertain in their output. The uncertainity in the solar irradiance can not be handled with the existing voltage control devices as they need to operate more frequently than usual which can cause recurring maintenance needs for these devices. Thus, to make solar PV more flexible and grid-friendly, smart inverters are being developed. Smart inverters have the capability of advanced sensing, communication, and controllability which can be utilized for voltage control. The research discusses how the inverters can be used to improve the grid profile by providing reactive power support to reduce the power losses and maintain voltages in their limits for a safer operation.