This project will perform a planning & operational study on a co-located wind farm and hydrogen energy system. Integrated energy systems (IES) of this genre need to be designed considering both the financial and technical constraints to demonstrate their feasibility rather than relying on additional incentives or favoring policies. REopt, an open source tool developed by NREL, will be adopted and improved to perform the study.

Papers:

1. *Li, H., *Rahman, J. and Zhang, J., Optimal Planning of Co-Located Wind Energy and Hydrogen Plants: A Techno-Economic Analysis, The Science of Making Torque from Wind (TORQUE 2022), Delft, Netherlands, June 1-3, 2022.

This project is to explore how machine learning could be leveraged to solve the optimal switching of reconfigurable microgrids in real time under both normal and extreme operations (e.g., grid contingencies, natural disasters, and cyber/physical attacks). A learn-to-reconfiguration (L2R) methodology will be developed to learn the relationship between microgrid state and optimal reconfiguration decisions with an end-to-end manner based on state-of-the-art deep learning models.

Papers:

1. *Rahman, J., *Jacob, R. A., Paul, S., Chowdhury, S. and Zhang, J., Reinforcement Learning Enabled Microgrid Network Reconfiguration Under Disruptive Events, IEEE Kansas Power and Energy Conference (KPEC), Manhattan, Kansas, April 25 - 26, 2022.

2. *Dabbaghjamanesh, M. and Zhang, J., Deep Learning-based Real-time Switching of Reconfigurable Microgrids, The Eleventh Conference on Innovative Smart Grid Technologies (ISGT 2020), Washington D.C., February 17-20, 2020.

The project is designing novel algorithms to create probabilistic solar power forecasts and automate their integration into power system operations. Adaptive reserves will dynamically adjust reserve levels conditional on meteorological and power system states. Risk-parity dispatch will be developed to produce optimal dispatch strategies by cost-weighting solar generation scenarios on forecast uncertainty. This project will test the integration of probabilistic solar forecasts into the Electric Reliability Council of Texas’ real-time operation environment through automated reserve and dispatch tools that can increase economic efficiency and improve system reliability.

Papers:

1. *Feng, C., Zhang, J., Zhang, W. and Hodge, B.-M., Convolutional Neural Networks for Intra-hour Solar Forecasting Based on Sky Image Sequences, Applied Energy, Vol. 310, 2022, pp. 118438. (PDF)

2. *Feng, C. and Zhang, J., SolarNet: A Sky Image-based Deep Convolutional Neural Network for Intra-hour Solar Forecasting, Solar Energy, Vol. 204, 2020, pp. 71-78. (PDF)

3. *Li, B., *Feng, C. and Zhang, J., Multi-Timescale Simulation of Non-Spinning Reserve in Wholesale Electricity Markets, 2021 IEEE Green Technologies Conference (GreenTech), Virtual, April 7-9, 2021.

4. *Feng, C., *Sun, M., Zhang, J., Doubleday, K., Hodge, B.-M. and Du, P., A Data-driven Method for Adaptive Reserve Requirements Estimation via Probabilistic Net Load Forecasting, IEEE Power & Energy Society General Meeting, Montreal, Canada, August 2-6, 2020.

5. *Feng, C., Yang, D., Hodge, B.-M. and Zhang, J., OpenSolar: Promoting the Openness and Accessibility of Diverse Public Solar Datasets, Solar Energy, Vol. 188, 2019, pp. 1369-1379. (PDF)

This project is advancing the state-of-the-art in solar forecasting technologies by developing short-term and day-ahead probabilistic solar power prediction capabilities. The proposed technology will be based on the big-data-driven, transformative IBM Watt-Sun platform, which will be driven by parallel computation-based scalable and fast data curation technology and multi-expert machine learning based model blending. The integration of validated probabilistic solar forecasts into the scheduling operations of both the Midcontinent and California Independent System Operators will be tested, via efficient and dynamic procurement of ramp product and regulation. Integration of advanced visualization of ramping events and associated alerts into their energy management systems and control room operations will also be researched and validated.

Papers:

1. *He, L., Zhang, J. and Hobbs, B., Estimation of Regulation Reserve Requirements in California ISO: A Data-driven Method, IEEE Power & Energy Society General Meeting, Orlando, FL, July 16-20, 2023. (Best Paper Award) (PDF)

2. *Li, B., Feng, C., Siebenschuh, C., Zhang, R., Spyrou, E., Krishnan, Hobbs, B. and Zhang, J., Sizing Ramping Reserve Using Probabilistic Solar Forecasts: A Data-Driven Method, Applied Energy, Vol. 313, 2022, pp. 118812. (PDF)

3. *Li, B. and Zhang, J., A Review on the Integration of Probabilistic Solar Forecasting in Power Systems, Solar Energy, Vol. 210, 2020, pp. 68-86. (Invited Paper) (PDF)

4. *Li, B., Zhang, J. and Hobbs, B., A Copula Enhanced Convolution for Uncertainty Aggregation, The Eleventh Conference on Innovative Smart Grid Technologies (ISGT 2020), Washington D.C., February 17-20, 2020.

5. *Cui, M. and Zhang, J., Estimating Ramping Requirements With Solar-Friendly Flexible Ramping Product in Multi-Timescale Power System Operations, Applied Energy, Vol. 225, 2018, pp. 27-41. (PDF)

This project will develop an innovative hybrid optimization method to solve the Security-Constrained Optimal Power Flow (SCOPF) problem formulated under the Challenge 1 of the ARPA-E Grid Optimization (GO) Competition. The base of the proposed optimization method will rely on a combination of formal mathematical optimization, metaheuristics, and the cross- entropy method, an approach that has proven to be successful in solving this kind of optimization problem, recently winning the SCOPF competition organized by the IEEE Working Group on Modern Heuristic Optimization (WGMHO).

Papers:

1. *Rahman, J., *Feng, C. and Zhang, J., A Learning-Augmented Approach for AC Optimal Power Flow, International Journal of Electrical Power and Energy Systems, Vol. 130, 2021, pp. 106908. (PDF)

2. *Rahman, J., *Feng, C. and Zhang, J., Machine Learning-Aided Security Constrained Optimal Power Flow, IEEE Power & Energy Society General Meeting, Montreal, Canada, August 2-6, 2020.

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.

Papers:

1. *Li, B., Sedzro, K., Fang, X., Hodge, B.-M. and Zhang, J., A Clustering-Based Scenario Generation Framework for Power Market Simulation with Wind Integration, Journal of Renewable and Sustainable Energy, Vol. 12, 2020, pp. 036301. (PDF)

2. *Cui, M., Krishnan, V., Hodge, B.-M. and Zhang, J., A Copula-Based Conditional Probabilistic Forecast Model for Wind Power Ramps, IEEE Transactions on Smart Grid, Vol. 10, Issue 4, 2019, pp. 3870-3882. (PDF)

3. *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, Vol. 10, Issue 2, 2019, pp. 1326-1338. (PDF)

4. *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)

5. *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)

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

Papers:

1. *Sun, M., *Feng, C. and Zhang, J., Conditional Aggregated Probabilistic Wind Power Forecasting Based on Spatio-temporal Correlation, Applied Energy, Vol. 256, 2019, pp. 113842. (PDF)

2. *Sun, M., *Feng, C., Chartan, E., Hodge, B.-M. and Zhang, J., A Two-Step Short-Term Probabilistic Wind Forecasting Methodology Based on Predictive Distribution Optimization, Applied Energy, Vol. 238, 2019, pp. 1497-1505. (PDF)

3. *Feng, C., *Sun, M., *Cui, M., Chartan, E., Hodge, B.-M. and Zhang, J., Characterizing Forecastability of Wind Sites in the United States, Renewable Energy, Vol 133, 2019, pp. 1352-1365. (PDF)

4. *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)

5. *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)

This project is developing load forecasting models (especially under extreme weather conditions) of different forecasting horizons at different levels of aggregation, by taking account of behind-the-meter (BTM) PV.

Papers:

1. *Feng, C. and Zhang, J., Assessment of Aggregation Strategies for Machine Learning based Short-Term Load Forecasting, Electric Power Systems Research, Vol. 184, 2020, pp. 106304. (PDF)

2. *Sun, M., *Feng, C. and Zhang, J., Factoring Behind-the-Meter Solar into Load Forecasting: Case Studies under Extreme Weather, The Eleventh Conference on Innovative Smart Grid Technologies (ISGT 2020), Washington D.C., February 17-20, 2020.

3. *Feng, C. and Zhang, J., Short-Term Load Forecasting With Different Aggregation Strategies, ASME 2018 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference, Paper No. DETC2018-86084, Quebec City, Canada, August 26-29, 2018.