Bourns College of Engineering

Electrical and Computer Engineering

Research Interests

Big Data

Big Data Anlytics in Smart Grid

Unleash full value of the complex data sets and transform the way we operate and plan for the smart grid. Read more →


Energy Efficient Smart Cities

Enabling energy efficient smart cities by seamlessly connecting buildings, people, and electric grid.Read more →


Water, Energy, Climate Nexus

Advance our understanding of the water-energy-climate nexus. Read more →


Electricity Market Design and Optimization

Design distribution electricity market and three-phase optimal power flow. Read more →

  • Big Data Anlytics in Smart Grid

    Background & Motivation

    Penetration of advanced sensor systems such as advanced metering infrastructure (AMI), high-frequency overhead and underground current and voltage sensors have been increasing significantly in power distribution systems over the past few years. According to U.S. energy information administration (EIA), the aggregated AMI installation experienced a 17 times increase from 2007 to 2012. The AMI usually collects electricity usage data every 15 minute, instead of once a month. This is a 3,000 fold increase in the amount of data utilities would have processed in the past. To unleash full value of the complex data sets, innovative big data algorithms need to be developed to transform the way we operate and plan for the smart grid.

    Computing Cluster


    The smart grid innovation laboratory is equipped with state-of-the-art Oracle Big Data Appliance.

    Cluster Specification

    Number of Nodes: 6

    Number of Core: 216

    288 TB of 7,200 RPM High Capacity SAS Disks

    768 GB DDR4 Memory

    CDH Enterprise Edition


  • Enhanced distribution system modeling

    Distribution network topology identification (phase connectivity identification, customer to transformer association estimation)

    Distribution network parameter estimation

    Spatio-temporal load forecast and renewable generation forecast

  • Enhanced distribution system monitoring

    Energy theft detection

    Three-phase state estimation in power distribution systems

    Predictive equipment maintenance

    Real-time visualization

  • Enhanced distribution system planning

    Valuation and optimization of DERs in power distribution network

    Diversification factor and load factor estimation

    Solar PV adoption forecast and EV adoption forecast

  • Electricity Market Design and Optimization


    In the past 20 years, wholesale power markets operating in transmission systems have been effective at coordinating the operations of thousands of centralized power plants. This coordination needs to be extended to the operations of millions of DERs. To do this efficiently, a Distribution system operator (DSO) managed electricity market seems to be a viable solution. Although the concept of a DSO-managed electricity market has been introduced, a key algorithm for operating the market is still in its infancy. This algorithm is three-phase optimal power flow (OPF), and it needs significant development.

    Current Work

    Design Integrated wholesale and retail market. The integrated market architecture is shown in the figure below.


    Develop DSO market, three-phase DCOPF and ACOPF.

    Research Highlight

    The proposed three-phase ACOPF algorithm is not only computationally efficient but also guarantees global optimality on all IEEE distribution test circuits

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  • Distributed Energy Resources Optimization and Valuation


    Driven by environmental regulations and rapidly falling renewable prices, the share of renewable generation in global electrical energy mix is expected to increase significantly over time. The intermittency of renewable resources has created new challenges in the transmission system operations.

    Energy storage system is well poised to mitigate uncertainties of renewable generation outputs. However, there are several challenges to the widespread deployment of energy storage. As identified in the U.S. Department of Energy report, the most crucial hurdle to storage adoption is how to ensure energy storage are cost competitive with other energy resources. To overcome this hurdle my research group developed a comprehensive optimization and valuation model (ESVOT) which allows energy storage to provide multiple electricity market products simultaneously.

    Energy Storage Optimization and Valuation Tool (ESVOT)

    ESVOT allows the user to conduct a comprehensive stochastic valuation of energy storage systems. In addition, ESVOT identifies the optimal energy storage integration location, size and technology for each customer.

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