Engineering topics

Yi, Ming

Publications and source records attributed to Yi, Ming.

21 records · Page 2

DOE OSTI2023

Net Load Forecasting With Disaggregated Behind-the-Meter PV Generation

As worldwide use of residential photovoltaic (PV) systems grows, system operators and utilities will need to transition from forecasting pure demand to forecasting net load with behind-the-meter (BTM) PV generation. However, PV generation can be difficult to predict and the measurements of PV generation from BTM residential systems are often invisible behind a measurement of the net load, making net load forecasting challenging. This paper proposes a novel two-stage framework for net load forecasting in areas with limited observability and high BTM PV generation. First, the profiles of observable customers are used to disaggregate the net load measurements into the pure load and PV generation. Then, separate models are used to forecast the PV generation and pure load individually, and the results are combined for a net load forecast. Further, this paper also proposes a compensator for correcting the error of the net load forecast, using historical forecast errors of the PV generation, pure load, and net load. The proposed framework is tested through two case studies for areas with high BTM PV penetration and less than 10% observable customers. The two-stage forecasting model is compared to two benchmark methods - a time series forecasting model, and a model that forecasts the net load directly using historical net load measurements. Results show that the proposed disaggregation-forecasting framework reduces the error of the net load forecast compared to both benchmark models. In addition, when the net load forecast error is periodic, the compensator can correct the error to improve the forecast accuracy.

14 SOLAR ENERGY↗

DOE OSTI2023

Magnetism and charge density wave order in kagome FeGe

Electron correlations often lead to emergent orders in quantum materials, and one example is the kagome lattice materials where topological states exist in the presence of strong correlations between electrons. This arises from the features of the electronic band structure that are associated with the kagome lattice geometry: flat bands induced by destructive interference of the electronic wavefunctions, topological Dirac crossings, and a pair of van Hove singularities. Various correlated electronic phases have been discovered in kagome lattice materials, including magnetism, charge density waves, nematicity, and superconductivity. Recently, a charge-density wave was discovered in the magnetic kagome FeGe, providing a platform for understanding the interplay between charge order and magnetism in kagome materials. Here, we observe all three electronic signatures of the kagome lattice in FeGe using angle-resolved photoemission spectroscopy. The presence of van Hove singularities near the Fermi level is driven by the underlying magnetic exchange splitting. Furthermore, we show spectral evidence for the charge-density wave as gaps near the Fermi level. Furthermore, our observations point to the magnetic interaction-driven band modification resulting in the formation of the charge-density wave, and indicate an intertwined connection between the emergent magnetism and charge order in this moderately-correlated kagome metal.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2023

Bayesian High-Rank Hankel Matrix Completion for Nonlinear Synchrophasor Data Recovery

Phasor measurement units (PMUs) provide high temporal-resolution synchrophasor measurements for power system monitoring and control. The frequent data quality issues, such as missing and bad data, prevent the incorporation of synchrophasor data in real-time operations. Most existing data-driven data recovery methods assume the power system dynamics can be approximated by a linear dynamical system, and the recovery performance degrades significantly when the power system is experiencing nonlinear dynamics during significant events. Here, this paper proposes a data-driven Bayesian nonlinear synchrophasor data recovery method (Ba-NSDR) that can recover a consecutive time period of simultaneous data losses or errors across all channels, even when the underlying system is highly nonlinear. The idea is to lift the Hankel matrix of the spatial-temporal synchrophasor data to a higher dimension such that the lifted Hankel matrix is low-rank in that space and can be processed with the kernel trick. Our proposed Bayesian method then infers the probabilistic distributions of synchrophasor from the partial observations. Some distinctive features of Ba-NSDR include an uncertainty index to measure the accuracy of the recovery result and the robustness to parameter selections. Our method is verified on both synthetic and recorded event datasets.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗