Search Results for author: Yishen Wang

Found 6 papers, 1 papers with code

Evaluating Load Models and Their Impacts on Power Transfer Limits

no code implementations7 Aug 2020 Xinan Wang, Yishen Wang, Di Shi, Jianhui Wang, Siqi Wang, Ruisheng Diao, Zhiwei Wang

Since the load dynamics have substantial impacts on power system transient stability, load models are one critical factor that affects the power transfer limits.


Two-stage WECC Composite Load Modeling: A Double Deep Q-Learning Networks Approach

no code implementations8 Nov 2019 Xinan Wang, Yishen Wang, Di Shi, Jianhui Wang, Zhiwei Wang

However, a detailed WECC CLM model typically has a high degree of complexity, with over one hundred parameters, and no systematic approach to identifying and calibrating these parameters.


Short-term Load Forecasting at Different Aggregation Levels with Predictability Analysis

no code implementations26 Mar 2019 Yayu Peng, Yishen Wang, Xiao Lu, Haifeng Li, Di Shi, Zhiwei Wang, Jie Li

Short-term load forecasting (STLF) is essential for the reliable and economic operation of power systems.

Load Forecasting

Probabilistic Load Forecasting via Point Forecast Feature Integration

no code implementations26 Mar 2019 Qicheng Chang, Yishen Wang, Xiao Lu, Di Shi, Haifeng Li, Jiajun Duan, Zhiwei Wang

In the first stage, all related features are utilized to train a point forecast model and also obtain the feature importance.

Feature Importance Load Forecasting

Submodular Load Clustering with Robust Principal Component Analysis

no code implementations20 Feb 2019 Yishen Wang, Xiao Lu, Yiran Xu, Di Shi, Zhehan Yi, Jiajun Duan, Zhiwei Wang

Traditional load analysis is facing challenges with the new electricity usage patterns due to demand response as well as increasing deployment of distributed generations, including photovoltaics (PV), electric vehicles (EV), and energy storage systems (ESS).

Load Forecasting

Model-Free Renewable Scenario Generation Using Generative Adversarial Networks

2 code implementations30 Jul 2017 Yize Chen, Yishen Wang, Daniel Kirschen, Baosen Zhang

We demonstrate that the proposed method is able to generate realistic wind and photovoltaic power profiles with full diversity of behaviors.

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