This study explores the impact of high-photovoltaic (PV) penetration on the inter-area oscillation modes of large-scale power grids. A series of dynamic models with various PV penetration levels are developed based on a detailed model representing the U.S. Eastern Interconnection (EI). Transient simulations are performed to investigate the change of inter-area oscillation modes with PV penetration. The impact of PV control strategies and parameter settings on inter-area oscillations is studied. This paper finds that as PV increases, the damping of the dominant oscillation mode decreases monotonically. It is also observed that the mode shape varies with the PV control strategy and new oscillation modes may emerge under inappropriate parameter settings in PV plant controls.

This letter proposed an approach to identify the change of inertia distribution in high renewable power systems. Using the footprints of electromechanical wave propagation at the distribution level, this approach provides a new and non-invasive way to aware the system inertia distribution for primary frequency response. Actual measurements and high renewable dynamic models validated effectiveness of the approach.

Nonsynchronous generators such as photovoltaics (PVs) are expected to undermine bulk power systems (BPSs)' frequency response at high penetration levels. Though the underlying mechanism has been relatively well understood, the accurate assessment and effective enhancement of the U.S. interconnections' frequency response under extra-high PV penetration conditions remains an issue. In this paper, the industry-provided full-detail interconnection models were further validated by synchrophasor frequency measurements and realistically-projected PV geographic distribution information were used to develop extra-high PV penetration scenarios and dynamic models for the three main U.S. interconnections, including Eastern Interconnection (EI), Western Electricity Coordinating Council (WECC), and Electric Reliability Council of Texas (ERCOT). Up to 65% instantaneous PV and 15% wind penetration were simulated and the frequency response change trend of each U.S. interconnection due to the increasing PV penetration level were examined. Most importantly, the practical solutions to address the declining frequency response were discussed. This paper will provide valuable guidance for policy makers, utility operators and academic researchers not only in the U.S. but also other countries in the world.

With the aid of global positioning system (GPS), synchronized measurement devices (SMDs) are increasingly deployed across power systems to monitor the status of electric grids by providing accurate measurement data along with unified time stamps. Unfortunately, GPS receivers tend to lose signal lock when certain uncontrollable and unpredictable factors arise. In order to investigate the presence of GPS signal loss (GSL) issues on measurement devices, analysis is performed on historical data from both phasor data concentrators and FNET/GridEye servers. Meanwhile, the impact of GSL on field measurement accuracy has not been previously explored in depth. Through analysis and experimental tests, this paper discovers angle drift caused by GSL, which consequently leads to the total vector error exceeding the IEEE standard C37.118.1-2011. Furthermore, a compensation method is proposed to rectify the angle drift and laboratory experiments demonstrate that the proposed method does effectively reduce angle drift and mitigate the impact of GSL in SMDs.

The distribution-level electric network frequency (ENF) extracted from an electric power signal is a promising forensic tool for multimedia recording authentication. Local characteristics in ENF signals recorded in different locations act as environmental signatures, which can be potentially used as a fingerprint for location identification. In this paper, a reference database is established for distribution-level ENF using FNET/GridEye system. An ENF identification method that combines a wavelet-based signature extraction and feedforward artificial neural network-based machine learning is presented to identify the location of unsourced ENF signals without relying on the availability of concurrent signals. Experiments are performed to validate the effectiveness of the proposed method using ambient frequency measurements at multiple geographic scales. Identification accuracy is presented, and the factors that affect identification performance are discussed.