Research Interests

1). Grid integration of electric vehicles

Advances in intelligent transportation systems, offer great promise to address these challenges and have the potential to revolutionize future transportation systems. In the last decade, the U.S. government has spurred efforts to boost the utilization of transportation electrification technologies, because of their low-pollution emissions, energy independence, and high fuel economy. The successful rollout of the transportation electrification is highly dependent upon the affordability, availability, quality and resilience of the services that our nation’s critical infrastructures can provide, such as electric vehicle charging facilities. In this project, a systematic investigation is proposed focusing on strategic planning and economic operation of EV charging facilities in a highly interdependent electrified transportation infrastructure with multidisciplinary complexities.

2). Large-Signal Stability Analysis and Enhancement of Converter-Dominated DC Microgrid

While DC microgrids have many well-understood advantages (e.g., simpler, more efficient and compact power conversion system as well as less copper consumption in the cables), the unique DC electric characteristics, such as direct P-V coupling (i.e., even a small load/generation change can lead to voltage flickers and equipment malfunctions) and low system inertia (i.e., very little overload capacity), pose great challenges to grid stability. Small-signal stability can only ensure the stability of the system at the equilibrium point, but the true boundary of the stability domain cannot be determined; hence there are major limitations in securing stability when the system has large disturbances. Existing large-signal analysis tools in the literature have either limited applicable ranges or non-rigorous theoretical foundations. Therefore, there is an urgent need to develop a fundamental knowledge base of large-signal stability analysis in converter-dominated DC microgrids and a comprehensive design guideline for DC grid stability.

The goal of this project is to (a) take a rigorous step toward deriving the sufficient criteria for large-signal global stability in DC microgrids with multiple distributed energy sources and constant power loads, which is still an unsolved puzzle because traditional small-signal stability analysis does not apply to converter-dominated power systems when a large disturbance occurs, such as a fault, a pulse power load, or load switching; and (b) investigate a systematic methodology to improve the global asymptotic stability of a converter-dominated DC microgrid in a theoretically sound yet easy-to-implement manner.

3). Energy Internet

The next-generation residential distribution system is a level playing field in terms of electricity transactions, where all residential customers have an equal opportunity. While legacy power system operations are solely driven by least-cost and reliability concerns, the Energy Internet innovations are completely reshaping the traditional views of our power generation, distribution, consumption, social environment, and business world. The proposed Energy Internet requires that our society move away from, or at least supplement, the traditional centralized generation, distribution, and consumption business model to one where every user can actively participate in the energy market. User participation is a major factor that has resulted in exponentially increasing innovation and ingenuity in the information technology sector (e.g., Amazon, eBay and Facebook).

If the ‘‘Information Internet’’ was the engine that powered our country’s economic growth in the last thirty years, then a similar and even more powerful Energy Internet will accomplish even more in the next several decades. In this context, the proposed new electricity market framework is analogous to the stock market or the business model of internet consumer-to-consumer (C2C) commerce such as eBay. The proposed work is first known major effort to advance fundamental knowledge of a next-generation retail electricity market framework with full residential customer (Energy Cells) participation.


In this project, we are developing a machine learning-enhanced design tool for the automated architectural configuration and performance evaluation of electrical power converters. This tool will help engineers consider a wider range of innovative concepts when developing new converters than would be possible via traditional approaches. This tool is expected to leverage a number of machine learning techniques--including decision trees, supervised learning and reinforcement learning--and is expected to reduce the cost and time required to develop new ultra-efficient power-converter designs.

5). Big Data Analytics of Smart Grid Applications

As the number of smart meters/sensors increases to more than hundreds of thousands, it is rather intuitive that the state-of-the-art centralized information processing architecture will no longer be sustainable under such a big data explosion. Hence, an innovative distributed data management system is urgently needed to facilitate the real-world deployment of a future residential distribution system.

In this project, we investigate a radically different approach through distributed software agents to translate the legacy centralized data storage and processing scheme to a completely distributed cyber-physical architecture. We further substantiate the proposed distributed data storage and processing framework on a proof- of-concept testbed using a cluster of low-cost and credit-card-sized single-board computers. Finally, we evaluate the proposed distributed framework and proof-of-concept testbed with a comprehensive set of performance measures.

6). Strengthening Power Grid Resilience to Natural Disasters using Data Analytics and Machine Learning

Electric power distribution networks have been a fundamental element and a large part of the power grid infrastructure. Distribution networks are the connecting point for most end users, distributed energy resources (DERs), and electric vehicles (EVs). Nearly 160 million customers in our nation are served via distribution networks.

The increasing penetration levels of EVs and DERs, the massive deployment of advanced metering infrastructure (AMI), and the adoption of flexible loads pose challenges and opportunities to our nation’s critical infrastructure. Severe weather events can lead to interruptions in this vital service and difficulties for users, which will become more significant as reliance on the grid grows. We are developing a suite of solution algorithms to strengthen our nation’s electric power distribution networks for greater preparedness and resilience to a variety of natural disasters (floods, storms, significant ice accumulation, and blizzards).