Research Interests

  • Power & Energy Systems: renewable energy grid integration, power system operations, demand response, microgrids, electricity market design

  • Renewable Energy: wind and solar forecasting, wind and solar ramp forecasting, load and netload forecasting, forecasting visualization, wind and solar resource assessment, wind farm design, distributed energy resources

  • Energy Management: building energy management, building to grid integration, battery thermal management, electric vehicle battery management

  • Big Data Analytics: energy data analytics, statistics, machine learning

  • Multidisciplinary Design Optimization: surrogate modeling, optimization, uncertainty quantification, design of experiment, probabilistic design, sensitivity analysis

  • Complex Engineered Systems: cyber-physical systems, value-driven design, system modeling, design, and optimization

Research Sponsors

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Current Research Projects

  • Providing Ramping Service with Wind to Enhance Power System Operational Flexibility

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With increasing wind power penetration in the electricity grid, system operators are recognizing the need for additional flexibility, and some are implementing new ramping products as a type of ancillary service. However, wind is generally thought of as causing the need for ramping services, not as being a potential source for the service. In this project, a multi- timescale unit commitment and economic dispatch model is developed to consider the wind power ramping product (WPRP). An optimized swinging door algorithm (OpSDA) with dynamic programming is applied to identify and forecast wind power ramps (WPRs). Designed as positive characteristics of WPRs, the WPRP is then integrated into the multi-timescale dispatch model that considers new objective functions, ramping capacity limits, active power limits, and flexible ramping requirements.

  • WindView: An Open Platform for Wind Energy Forecast Visualization

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This project is to create an open situational awareness and decision support platform “WindView”, that provides grid operators with knowledge on the state and performance of their power system, with an emphasis on wind energy. The focus will be on utilizing advanced visualization to display pertinent information, extracted through computational techniques, from wind power forecasts for high-wind penetration systems

  • Battery Thermal Management Systems for Electric Vehicles

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Thermal management plays a significant role in the life, performance, safety, and cost of the lithium-ion battery modules in electric vehicles. Battery thermal management systems (BTMS) aim to improve the temperature uniformity among all battery cells, and to prevent battery cells from very high temperature which may likely cause their explosion. This project is to develop a hybrid approach by combining flat heat pipe, phase change material, and air cooling through modeling, experiments, and optimization.

  • Stochastic Security-Constrained Unit Commitment and Economic Dispatch

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A stochastic unit commitment and economic dispatch model that considers stochastic variables at multiple operational timescales is developed. It involves four distinct stages: stochastic day-ahead security-constrained unit commitment, stochastic real-time security-constrained unit commitment, stochastic real-time security-constrained economic dispatch, and deterministic automatic generation control.

  • Ramp Forecasting and Detection in Wind Power, Solar Power, and Netload

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An optimized swinging door algorithm (OpSDA) is developed to improve the state of the art in ramping detection. The swinging door algorithm (SDA) is utilized to segregate power data through a piecewise linear approximation. A dynamic programming algorithm is performed to merge adjacent segments with the same ramp changing direction.

  • Watt-sun: A Multi-scale, Multi-Model, Machine-Learning Solar Forecasting Technology

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Solar forecasting is a challenging task, since solar power generation is present in both the transmission and the distribution sides of the grid. A multi-scale, multi-model, machine-learning solar forecasting technology is in development. This project is (1) developing a scalable, cost-effective technology platform for improved solar forecasting by using big-data information processing technologies; and (2) developing a suite of generally applicable, value-based metrics for deterministic and probabilistic solar forecasting for a comprehensive set of scenarios (different time horizons, applications, etc.), which can assess the economic and reliability impact of improved solar forecasting.

Past Research Projects

  • Wind Forecast Improvement Project (WFIP)

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Short-term wind power forecasting plays an important role in grid operations for balancing supply and demand in the electric power system. The improved forecasting system consists of an ensemble of high-resolution rapidly updated numerical weather prediction models. Statistical analysis results show that the improved forecasting systems is more accurate than the current short-term wind power forecasts used by ERCOT. The economic analysis of using the improved forecasts are quantified, showing reductions in both unit commitment costs and reserve requirement costs.

  • Wind Farm Design and Optimization

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A new methodology, the Unrestricted Wind Farm Layout Optimization (UWFLO), that addresses critical aspects of optimal wind farm planning is developed. This methodology simultaneously determines the optimum farm layout and the appropriate selection of turbines (in terms of their rotor diameters) that maximizes the net power generation. A standard analytical wake model has been used to account for the velocity deficits in the wakes created by individual turbines. The wind farm power generation model is validated against data from a wind tunnel experiment on a scaled down wind farm. The complex nonlinear optimization problem presented by the wind farm model is effectively solved using constrained Particle Swarm Optimization (PSO).

  • Wind Resource Assessment

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A smooth multivariate wind distribution model is developed to capture the coupled variation of wind speed, wind direction, and air density. The Multivariate and Multimodal Wind distribution (MMWD) model is successfully applied to model (i) the distribution of wind speed (univariate); (ii) the distribution of wind speed and wind direction (bivariate); and (iii) the distribution of wind speed, wind direction, and air density (multivariate).

  • Surrogate Modeling and Applications to Complex System Design

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We developed a high fidelity surrogate modeling technique that we call the Adaptive Hybrid Functions (AHF). The AHF formulates a reliable Crowding Distance-Based Trust Region (CD-TR), and adaptively combines the favorable characteristics of different surrogate models. The weight of each contributing surrogate model is determined based on the local measure of accuracy for that surrogate model in the pertinent trust region. Such an approach is intended to exploit the advantages of each component surrogate.

  • Domain Segmentation based on Uncertainty in the Surrogate (DSUS)

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In the practice of engineering, where predictive models are pervasively used, the knowledge of the level of modeling error at any region inside the design space is uniquely helpful for design exploration and model improvement. The lack of methods that can explore the spatial variation of surrogate error levels in a wide variety of surrogates (i.e., model-independent methods) leaves an important gap in our capacity of design domain exploration. We develop a novel framework, called Domain Segmentation based on Uncertainty in the Surrogate (DSUS), to segregate the design domain based on the level of local errors.

  • Mixed-Discrete Particle Swarm Optimization

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A modification of the Particle Swarm Optimization (PSO) algorithm was developed, which can adequately address system constraints while dealing with mixed-discrete variables. Continuous search (particle motion), as in conventional PSO, was implemented as the primary search strategy; subsequently, the discrete variables were updated using a deterministic nearest-feasible-vertex criterion. This approach is expected to alleviate the undesirable difference in the rates of evolution of discrete and continuous variables.