Skills

Python, MATLAB, R, Julia

Power System Analysis

GAMS, AMPL, CPLEX, Gurobi, JuMP, YALMIP

Experience

 
 
 
 
 
June 2017 – Present
San Jose, CA, USA

Research Engineer

GEIRI North America

AI & System Analytics Group

Working with Dr. Di Shi at on the following topics:

  • Machine learning and PMU based load modeling
  • AI-based oscillation detection and control
  • Commercial hybrid AC/DC microgrid planning and operation
 
 
 
 
 
June 2015 – September 2015
Lemont, IL, USA

Research Aide

Argonne National Laboratory

Energy Systems Division

Worked with Dr. Audun Botterud, Dr. Zhi Zhou and Dr. Cong Liu on renewable integration with stochastic unit commitment

 
 
 
 
 
June 2014 – September 2014
Cambridge, MA, USA

Research Internship

Mitsubishi Electric Research Laboratories

Data Analytics Group

Worked with Dr. Hongbo Sun on distribution system reconfiguration with energy storage

 
 
 
 
 
September 2011 – June 2017
Seattle, WA, USA

Research Assistant

Department of Electrical Engineering, University of Washington

Renewable Energy Analysis Lab (REAL LAb)

Worked with Prof. Daniel Kirschen on the following topics:

  • Energy storage operation and planning in electricity markets
  • Renewable integration with stochastic unit commitment
  • Scenario generation and reduction
  • Energy forecasting

Selected Publications

With the increasing complexity of modern power systems, conventional dynamic load modeling with ZIP and induction motors (ZIP + IM) is no longer adequate to address the current load characteristic transitions. In recent years, the WECC composite load model (WECC CLM) has shown to effectively capture the dynamic load responses over traditional load models in various stability studies and contingency analyses. 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. Enabled by the wide deployment of PMUs and advanced deep learning algorithms, proposed here is a double deep Q-learning network (DDQN)-based, two-stage load modeling framework for the WECC CLM. This two-stage method decomposes the complicated WECC CLM for more efficient identification and does not require explicit model details. In the first stage, the DDQN agent determines an accurate load composition. In the second stage, the parameters of the WECC CLM are selected from a group of Monte-Carlo simulations. The set of selected load parameters is expected to best approximate the true transient responses. The proposed framework is verified using an IEEE 39-bus test system on commercial simulation platforms.
IEEE Transactions on Smart Grid, 2020

Growing model complexities in load modeling have created high dimensionality in parameter estimations, and thereby substantially increasing associated computational costs. In this paper, a tensor-based method is proposed for identifying composite load modeling (CLM) parameters and for conducting a global sensitivity analysis. Tensor format and Fokker-Planck equations are used to estimate the power output response of CLM in the context of simultaneously varying parameters under their full parameter distribution ranges. The proposed tensor structure is shown as effective for tackling high-dimensional parameter estimation and for improving computational performances in load modeling through global sensitivity analysis.
IEEE Transactions on Smart Grid, 2020

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). At the transmission system, despite of irregular load behaviors at different areas, highly aggregated load shapes still share similar characteristics. Load clustering is to discover such intrinsic patterns and provide useful information to other load applications, such as load forecasting and load modeling. This paper proposes an efficient submodular load clustering method for transmission-level load areas with robust principal component analysis.
IEEE PESGM, 2019

As microgrids have advanced from early prototypes to relatively mature technologies, converting data center integrated commercial buildings to microgrids provides economic, reliability and resiliency enhancements for the building owners. Thus, microgrid design and economically sizing distributed energy resources (DER) are becoming more demanding to gain widespread microgrids commercial viability. In this paper, an optimal DER sizing formulation for a hybrid AC/DC microgrid configuration has been proposed to leverage all benefits that AC or DC microgrid could solely contribute.
IEEE PESGM, 2018

Scenario generation is an important step in the operation and planning of power systems with high renewable penetrations. In this work, we proposed a data-driven approach for scenario generation using generative adversarial networks, which is based on two interconnected deep neural networks. Compared with existing methods based on probabilistic models that are often hard to scale or sample from, our method is data-driven, and captures renewable energy production patterns in both temporal and spatial dimensions for a large number of correlated resources. We also illustrate how to generate scenarios based on different conditions of interest by using labeled data during training.
IEEE Transactions on Power Systems, 2018

As the cost of battery energy storage continues to decline, we are likely to see the emergence of merchant energy storage operators. These entities will seek to maximize their operating profits through strategic bidding in the day-ahead electricity market. One important parameter in any storage bidding strategy is the state-of-charge at the end of the trading day. Because this final state-of-charge is the initial state-of-charge for the next trading day, it has a strong impact on the profitability of storage for this next day. This paper proposes a look-ahead technique to optimize a merchant energy storage operator’s bidding strategy considering both the day-ahead and the following day.
IEEE Transactions on Sustainable Energy, 2017

Stochastic programming methods have been proven to deal effectively with the uncertainty and variability of renewable generation resources. However, the quality of the solution that they provide (as measured by cost and reliability metrics) depends on the accuracy and the number of scenarios used to model this uncertainty and variability. Scenario reduction techniques are used to manage the computational burden by selecting representative scenarios. The common drawback of existing scenario reduction techniques is that the number of representative scenarios is a user-defined parameter. We propose a scenario reduction algorithm based on submodular function optimization to endogenously optimize the number of scenarios as well as rank these scenarios.
IEEE Transactions on Power Systems, 2017

Recent Publications

More Publications

. Two-stage WECC Composite Load Modeling: A Double Deep Q-Learning Networks Approach. IEEE Transactions on Smart Grid, 2020.

Preprint PDF Project

. Global Sensitivity Analysis in Load Modeling via Low-rank Tensor. IEEE Transactions on Smart Grid, 2020.

Preprint PDF Project

. Mathematical representation of the WECC composite load model. MPCE, 2019.

Preprint Project

. Robust Time-Varying Synthesis Load Modeling in Distribution Networks Considering Voltage Disturbances. IEEE Transactions on Power Systems, 2019.

Preprint PDF Project

. Probabilistic Load Forecasting via Point Forecast Feature Integration. IEEE ISGT ASIA, 2019.

Preprint PDF Project

. Short-term Load Forecasting at Different Aggregation Levels with Predictability Analysis. IEEE ISGT ASIA, 2019.

Preprint PDF Project

. Sizing battery storage for islanded microgrid systems to enhance robustness against attacks on energy sources. MPCE, 2019.

PDF

. Submodular Load Clustering with Robust Principal Component Analysis. IEEE PESGM, 2019.

Preprint PDF Project

Contact

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