Research Interests

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

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

  • Energy Management: transactive home energy management, building to grid integration, energy storage, battery power and thermal management

  • Big Data Analytics: data-driven design, energy data analytics, statistics, data visualization, machine learning

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

  • Complex Engineered Systems: cyber-physical systems, energy 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). 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.


  1. *Cui, M., Zhang, J., Wang, Q., Krishnan, V. and Hodge, B.-M., A Data-Driven Methodology for Probabilistic Wind Power Ramp Forecasting, IEEE Transactions on Smart Grid, 2017. (in press) (PDF)

  2. *Cui, M., *Feng, C., *Wang, Z. and Zhang, J., Statistical Representation of Wind Power Ramps Using a Generalized Gaussian Mixture Model, IEEE Transactions on Sustainable Energy, Vol. 9, Issue 1, 2018, pp. 261-272. (PDF)

  3. *Cui, M., Zhang, J., Wu, H. and Hodge, B.-M., Wind-Friendly Flexible Ramping Product Design in Multi-Timescale Power System Operations, IEEE Transactions on Sustainable Energy, Vol. 8, Issue 3, 2017, pp. 1064-1075. (PDF)

  • 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


  1. *Feng, C., *Cui, M., Hodge, B.-M. and Zhang, J., A Data-Driven Multi-Model Methodology with Deep Feature Selection for Short-Term Wind Forecasting, Applied Energy, Vol. 190, 2017, pp. 1245-1257. (PDF)

  2. *Feng, C., Chartan, E., Hodge, B.-M. and Zhang, J., Characterizing Time Series Data Diversity for Wind Forecasting, The 4th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT 2017), Austin, TX, December 5-8, 2017. (Best Student Paper Award)

  • 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.


  1. *Wang, X., *Li, M., *Liu, Y., Sun, W., Song, X. and Zhang, J., Surrogate based Multidisciplinary Design Optimization of Lithium-ion Battery Thermal Management System in Electric Vehicles, Structural and Multidisciplinary Optimization, Vol. 56, Issue 6, 2017, pp. 1555-1570. (PDF)

  2. *Liu, Y., *Li, M. and Zhang, J., An Experimental Parametric Study of Air-Based Battery Thermal Management System for Electric Vehicles, ASME 2017 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference, Paper No. DETC2017-67841 , Cleveland, Ohio, August 6-9, 2017.

  • 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.


  1. Wu, H., Krad, I., Florita, A., Hodge, B.-M., Ibanez, E., Zhang, J. and Ela, E., Stochastic Multi-timescale Power System Operations with Variable Wind Generation, IEEE Transactions on Power Systems, Vol. 32, Issue 5, 2017, pp. 3325-3337. (PDF)

  • 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.


  1. *Cui, M., Zhang, J., *Feng, C., Florita, A., Sun, Y. and Hodge, B.-M., Characterizing and Analyzing Ramping Events in Wind Power, Solar Power, Load, and Netload, Renewable Energy, Vol. 111, 2017, pp. 227-244. (PDF)

  2. *Cui, M., Zhang, J., Florita, A., Hodge, B.-M., Ke, D. and Sun, Y., An Optimized Swinging Door Algorithm for Identifying Wind Ramping Events, IEEE Transactions on Sustainable Energy, Vol. 7, Issue 1, 2016, pp. 150-162. (PDF)

  3. *Cui, M., Ke, D., Sun, Y., Gan, D., Zhang, J. and Hodge, B.-M., Wind Power Ramp Event Forecasting Using a Stochastic Scenario Generation Method, IEEE Transactions on Sustainable Energy, Vol. 6, Issue 2, 2015, pp. 422-433. (PDF)

  • 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.


  1. *Cui, M., Zhang, J., Hodge, B.-M., Lu, S. and Hamann, H. F., A Methodology for Quantifying Reliability Benefits from Improved Solar Power Forecasting in Multi-Timescale Power System Operations, IEEE Transactions on Smart Grid, 2017. (in press) (PDF)

  2. Zhang, J., Hodge, B.-M., Lu, S., Hamann, H. F., Lehman, B., Simmons, J., Campos, E., Banunarayanan,V., Black, J. and Tedesco, J., Baseline and Target Values for Regional and Point PV Power Forecasts: Toward Improved Solar Forecasting, Solar Energy, Vol. 122, 2015, pp. 804-819. (PDF)

  3. Zhang, J., Florita, A., Hodge, B.-M., Lu, S., Hamann, H. F., Banunarayanan, V. and Brockway, A., A Suite of Metrics for Assessing the Performance of Solar Power Forecasting, Solar Energy, Vol. 111, 2015, pp. 157-175. (PDF)

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.


  1. Zhang, J., *Cui, M., Hodge, B.-M., Florita, A. and Freedman, J., Ramp Forecasting Performance from Improved Short-Term Wind Power Forecasting Over Multiple Spatial and Temporal Scales, Energy, Vol. 122, 2017, pp. 528-541. (PDF)

  2. Freedman, J. et al., The Wind Forecast Improvement Project (WFIP): A Public/Private Partnership for Improving Short Term Wind Energy Forecasts and Quantifying the Benefits of Utility Operations - the Southern Study Area, DOE EERE Wind Energy Technologies Office, April 2014.

  • 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).


  1. Chowdhury, S., Zhang, J., Messac, A. and Castillo, L., Optimizing the Arrangement and the Selection of Turbines for Wind Farms Subject to Varying Wind Conditions, Renewable Energy, Vol. 52, 2013, pp. 273-282. (PDF)

  2. Zhang, J., Chowdhury, S., Messac, A. and Castillo, L., A Response Surface-Based Cost Model for Wind Farm Design, Energy Policy, Vol. 42, 2012, pp. 538-550. (PDF)

  3. Chowdhury, S., Zhang, J., Messac, A. and Castillo, L., Unrestricted Wind Farm Layout Optimization (UWFLO): Investigating Key Factors Influencing the Maximum Power Generation, Renewable Energy, Vol. 38, Issue 1, 2012, pp. 16-30. (PDF) (Best Paper Award)

  4. Messac, A., Chowdhury, S. and Zhang, J., Characterizing and Mitigating the Wind Resource-based Uncertainty in Farm Performance, Journal of Turbulence (special issue on Turbulence and Wind Energy), Vol. 13, Issue 13, 2012, pp. 1-26. (PDF)

  • 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).


  1. Zhang, J., Chowdhury, S., Messac, A. and Hodge, B.-M., A Hybrid Measure-Correlate-Predict Method for Long-Term Wind Condition Assessment, Energy Conversion and Management, Vol. 87, 2014, pp. 697-710. (PDF)

  2. Zhang, J., Chowdhury, S. and Messac, A., A Comprehensive Measure of the Energy Resource: Wind Power Potential (WPP), Energy Conversion and Management, Vol. 86, 2014, pp. 388-398. (PDF)

  3. Zhang, J., Chowdhury, S., Messac, A. and Castillo, L., A Multivariate and Multimodal Wind Distribution Model, Renewable Energy, Vol. 51, 2013, pp. 436-447. (PDF)

  • 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.


  1. Zhang, J., Chowdhury, S., Messac, A., Zhang, J.Q. and Castillo, L., Adaptive Hybrid Surrogate Modeling for Complex Systems, AIAA Journal, Vol. 51, Issue 3, 2013, pp. 643-656. (PDF)

  2. Zhang, J., Chowdhury, S. and Messac, A., An Adaptive Hybrid Surrogate Model, Structural and Multidisciplinary Optimization, Vol. 46, Issue 2, 2012, pp. 223-238. (PDF)

  • 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.


  1. Zhang, J., Chowdhury, S., Mehmani, A. and Messac, A., Characterizing Uncertainty Attributable to Surrogate Models, Journal of Mechanical Design, Vol. 136, Issue 3, 2014, pp. 031004. (PDF)

  • 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.


  1. Chowdhury, S., Tong, W., Messac, A. and Zhang, J., A Mixed-Discrete Particle Swarm Optimization Algorithm with Explicit Diversity-Preservation, Structural and Multidisciplinary Optimization, Vol. 47, Issue 3, 2013, pp. 367-388. (PDF)