The Power Of Synchrophasors Electric Grid Monitoring Control And Optimization PDF Download

Recent growth in deployment of distributed energy resources (DERs), energy storage systems, and advanced grid control schemes have increased the levels of variability in generation and load conditions over the electric transmission and distribution system. Microgrids are distribution level power systems that operate as a single controllable system in a grid connected or islanded (disconnected) mode. The balance between supply and demand of power is one of the most important requirements of microgrid management. The ability to control such a system hence becomes a very important task in order to stably and smoothly operate the microgrid. One such elegant method to overcome these challenges is the use of the phasor measurement units (PMUs) for wide-area distributed networks capable of measuring time-synchronized phasor (synchrophasor) data at high rates in the multiples of the main AC frequency (up to 240 Hz for a 60 Hz signal). The importance to use synchrophasors for this application is two-fold: Firstly, the ability for high frequency and synchronized sampling significantly enhances (transient) grid monitoring and also improves performance of the state estimation or (recursive) prediction that can be used for real-time control. Secondly, synchrophasor measurements can be used to coordinate the power exchange between cooperative multiple loads (microgrids) using the time-synchronized measurements. However, due to the high sampling rates of synchrophasor data, it becomes an unwieldy task to process such huge data from multiple locations. Local (signal) processing of PMU data to detect, store and classify events in the electric grid at the point of measurement is one of the main contributions of the dissertation. To address the demand and supply problem in a microgrid, (recursive) demand prediction and (optimal) generation dispatch techniques using synchrophasors are discussed in the dissertation. In order to design high speed control algorithms for such microgrid systems, derivation of a system model via system identification using data driven techniques would be more convenient than derivation via swing equations. Hence, the dissertation contributes in the estimation of a low order linear time invariant (LTI) model for active and reactive power flow (derived from the PMUs) through the microgrid. Using the derived models, it is shown how real-time PMU measurements can further be used in the islanding (power flow and angle) control of microgrids. Finally, robustness of missing PMU data for control applications is addressed in the dissertation.