UC San Diego

The rapid adoption of large and renewable Distributed Energy Resources (DERs) such as Photovoltaics (PV), wind generation and utility or customer owned Battery Energy Storage Systems (BESS), and distributed energy demands such as Electric Vehicles (EV) in the electric grid has increased the volatility of generation and load conditions in the electric grid. In addition, electrification of large consumer loads such as heating and transportation by Electric Vehicles (EVs) has also increased the power demands on the grid. As the traditional one-way power flow of generation-transmission-distribution-consumer model is being replaced by an exchange of power between Behind the Meter (BTM) assets such as Microgrids, EV, PV, and front of the Meter (FTM) utility assets, the role of utilities and Distributed System Operators (DSOs) that monitor the electric grid and utilities has become more challenging to deal with the increasing variable and less predictable load profiles in the electric grid. The main challenge in grid monitoring and operations is to meet the variable load demand to balance power flow, while maximizing renewable energy resources capacity and improving reliability. Traditionally, balancing of power flow is achieved by a combination of predictive scheduling of generation and real-time control of generation assets by droop control based on the AC-frequency of the grid. The latter becomes ineffective as less traditional inertia-based power generation assets and more inverter-based generation is introduced on the grid. Furthermore, the ability to push power flow through the grid is limited by impedance of lines and transformers used in the transmission and distribution network. Increases in power flow or impedance can drive up voltage through the network and limit the capacity and reliability of power delivery. One such solution to help monitoring the grid to help with the above mentioned challenges is the use of synchrophasor data measured by Phasor Measurement Units (PMUs). Due to the ability to synchronize voltage measurements over large distances and measuring data at high sampling rates (typically around 60 Hz), PMUs can be used in real-time grid monitoring, event detection, protection and control. The economic benefits of real-time voltage, current and line monitoring using synchrophasor data are immense. Synchrophasor data can replace the traditional Action Remedial Schemes that are used for islanding purpose to minimize outages. The use of real-time synchrophasor data will improve planned islanding by dynamically determining then boundaries of the island according to the prevailing system conditions. PMUs can also be used for real-time congestion management applications to provide immense savings to utilities and their customers.

Although, deploying PMUs at every bus of a given network can be seen as a trivial solution to the grid monitoring problem, the costs associated with procurement and deployment of PMUs makes this solution infeasible for large scale networks. Furthermore, processing synchrophasor data from a large number of PMUs in a network becomes more challenging as the network grows larger in size. Hence, a prudent solution for grid monitoring is to strategically place PMUs over the network and use the network model for voltage estimation, also known as state estimation in field of power network. The major challenges with the proposed solution is the effect of PMU measurement noise on the obtained voltage estimates and uncertainties in the network model.This thesis aims to provide solutions for both selection of location of PMUs and perform state estimation to strengthen grid operations reliability. One of the main contributions of this dissertation is to optimally place PMUs across any given network such that the effect of PMU measurement noise on the voltage estimation is minimized. Another contribution is to select PMU locations such that the state estimation algorithm is robust to network model uncertainties in impedance information. Using the derived voltage estimates and the real-time synchrophasor measurements, it is shown how PMUs can be used in event detection and real-time monitoring of line admittances. Since synchrophasor data is important for real-time grid monitoring, PMU data dropouts can cause disruption to grid monitoring, protection and control. This dissertation also proposes a physics-informed learning-based solution to generate missing PMU data to make grid monitoring robust to PMU data dropouts.