Yaping Li
Electric Power Research Institute
8 Papers
1 Citations
Yaping Li is an academic researcher from Electric Power Research Institute. The author has contributed to research in topics: Demand response & Load balancing (electrical power). The author has an hindex of 3, co-authored 3 publications.
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Papers
Quantifying Flexibility of Commercial and Residential Loads for Demand Response using Setpoint Changes
Rongxin Yin,Emre Can Kara,Emre Can Kara,Yaping Li,Nicholas DeForest,Ke Wang,Taiyou Yong,Michael Stadler +7 more
TL;DR: In this paper, the authors presented a novel demand response estimation framework for residential and commercial buildings using a combination of EnergyPlus and two-state models for thermostatically controlled loads, which can predict DR potential with 80-90% accuracy for more than 90% of data points.
276
Modeling study on flexible load's demand response potentials for providing ancillary services at the substation level
TL;DR: In this paper, a methodology for load aggregation based on the prioritization of loads according to their flexibility is presented, where different flexible load types are categorized as thermostatically controlled loads (TCL), urgent non-TCL, non-urgent non-CTL, and battery-based loads.
63
A robust offering strategy for wind producers considering uncertainties of demand response and wind power
TL;DR: Results demonstrate that the proposed bi-objective optimization approach enables the wind power producer to select appropriate offering decisions with respect to uncertainties, and shows that utilizing demand response resource to mitigate wind power deviations can increase a wind power Producer’s profit and reduce potential risks.
52
N-1 static security assessment method for power grids with high penetration rate of renewable energy generation
TL;DR: In this article , a new N-1 static security assessment method based on deep convolutional neural network (DCNN) for the power grid with a high penetration rate of renewable energy generation is proposed.
15
Day-ahead holiday load forecast based on pattern sequence similarity and random forest
Kedong Zhu,Yaping Li,Xiaorui Guo,Jiantao Liu,Gang Wang +4 more
- 29 Jul 2022
TL;DR: In this article , a novel day-ahead holiday load forecast is proposed by means of pattern sequence similarity and random forest, which can be splitted into daily per-unit curve and daily power external value.
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