As PMU data become more available and increasingly reliable, these devices are found in new roles within control systems, such as remedial action schemes and early warning detection systems. PMU datastreams inform wide-area state estimation, monitor area control error, and facilitate event detection in real time. Phasor measurement units (PMUs) provide high-fidelity data that improve situation awareness of electric power grid operations.
Examples for positive sequence transmission and three-phase distribution systems, including actual 75k+ nodes Eastern Interconnection transmission test cases and 8k+ nodes taxonomy distribution test cases, are solved from arbitrary initial guesses to demonstrate the efficacy of our approach. The proposed circuit models and formalism enable the extension and application of circuit simulation techniques to solve for the steady-state solution with excellent robustness of convergence. In this paper, we describe our equivalent circuit formulation approach with current and voltage variables that models both the positive sequence network of the transmission grid and three-phase network of the distribution grid without loss of generality. Due to the non-linear nature of the problem, it is difficult for alternating current (AC) power flow and three-phase power flow analyses to ensure convergence to the correct physical solution, particularly from arbitrary initial conditions, or when evaluating a change (e.g. At present, disparate methods exist for steady-state analysis of the transmission (power flow) and distribution power grid (three-phase power flow). Robust simulation is essential for reliable operation and planning of transmission and distribution power grids. In the results section we demonstrate the efficacy of this framework for black start by ensuring a feasible crank path with voltage and frequency within bounds, while further assisting in synchronization of two disconnected sub-grids during the re-energization process.
Streamcast update#
The proposed framework maintains an online network topology of the localized grid that can continuously sample measurements and update the grid model, thereby circumventing SCADA based central control. In this paper, we introduce an online framework for localized grid restoration that validates and updates a pre-defined crank path in real-time based on the vital grid states of voltages, currents and frequency. This is also true for grids in developing countries as well as for many distribution grids that lack SCADA. Generally, the restoration of grid during a blackout is achieved with the help of Supervisory Control and Data Acquisition System (SCADA) based central control however, with the threat of cyber-blackouts, this presumption of an available and secure SCADA system is not valid. Secure and fast grid restoration from a collapsed state is increasingly critical as blackouts are becoming a common occurrence around the globe. We demonstrate the efficacy of this approach by simulating the effects of different control actions including automatic generation control (AGC), automatic voltage (AVR) and over excitation limits on large systems. The event-driven approach offers a fast simulation framework capable of scaling to large systems by taking time-steps based on proceeding events and uses a robust power flow simulator, Simulation with Unified Grid Analyses and Renewables (SUGAR), to solve for the quasi-steady state. In this methodology, the effect of fast transient control actions is captured within the quasi-steady state while a time-dependent outer loop handles slow transients that dynamically change over time.
Streamcast simulator#
We introduce a robust event-driven simulator to mimic dynamic simulation that exploits the inherent temporal sparsity by solving for the quasi-steady state of the grid using frequency-dependent power flow at each time-step. The power grid is a dynamic system encompassing numerous control devices with varying degrees of time constants, posing a challenge for dynamic simulators to efficiently step through time.
0 kommentar(er)
