Project Description

New Challenges in the Next Generation of Statistical Algorithms for Modern Power Systems

A smart grid is an electrical power grid that is enhanced with communications and networking, computing, control, and signal processing technologies. The key to the success of the smart grid lies in developing effective techniques to make it more secure, with respect to detecting anomalies and attacks, and more profitable, with respect to more efficient energy management. This project aims to exploit new statistics and learning techniques including survey sampling techniques, functional time series models, robust statistics and LASSO based approaches such as online network LASSO and group LASSO. Novel statistics and learning based approach will be developed to tackle the several key problems in the modern power grid, i.e., (i) bad data injection detection to make it secure, (ii) load and generation forecasting to enable more effective power management, and (iii) LASSO based approaches to enable cooperative microgrids. The focus is on the modern power system, i.e., the smart grid, with distributed, renewable energy sources and microgrids.

Sept. 1, 2017 ~ Aug. 31, 2022

Project Team

• Guanqun (Vivian) Cao

• Shiwen Mao

• R. Mark Nelms

Related Publications (book and dissertation)

• L. Wang, Deep Learning-based Load Forecasting and Monitoring in the Smart Grid, PhD Dissertation, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, Nov. 2021

• N. Tang, LASSO Based Schemes for Solar Energy Forecasting, PhD Dissertation, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, May 2021

• Y. Wang, S. Mao, and R.M. Nelms, Online Algorithms for Optimal Energy Distribution in Microgrids. Springer Briefs Series, New York, NY: Springer, 2015. DOI: 10.1007/978-3-319-17133-3. ISBN: 978-3-319-17132-6.

Related Publications (journal & magazine)

• L. Wang, S. Mao, B. Wilamowski, and R. M. Nelms, “Pre-trained models for non-intrusive appliance load monitoring,” IEEE Transactions on Green Communications and Networking, Special Issue on Communications and Computing for Green Industrial IoT and Smart Grids, to appear. DOI: 10.1109/TGCN.2021.3087702.

• Y. Wang, F. Xu, S. Mao, S. Yang, and Y. Shen, "Adaptive online power management for more electric aircraft with hybrid energy storage systems,” IEEE Transactions on Transportation Electrification, Special Issue on More Electric Aircraft, vol.6, no.4, pp.1780-1790, Dec. 2020. DOI: 10.1109/TTE.2020.2988153.

• L. Wang, S. Mao, B. Wilamowski, and R.M. Nelms, "Ensemble learning for load forecasting,” IEEE Transactions on Green Communications and Networking, vol.4, no.2, pp.616-628, June 2020. DOI: 10.1109/TGCN.2020.2987304.

• H. Zou, Y. Wang, S. Mao, F. Zhang, and X. Chen, “Distributed online energy management in inter-connected microgrids,” IEEE Internet of Things Journal, vol.7, no.4, pp.2738-2750, Apr. 2020. DOI: 10.1109/JIOT.2019.2957158.

• H. Zou, S. Mao, Y. Wang, F. Zhang, X. Chen, and L. Cheng, “A survey of energy management in interconnected multi-microgrids,” IEEE Access Journal, Special Section on Urban Computing and Intelligence, vol.7, no.1, pp.72158-72169, June 2019. DOI: 10.1109/ACCESS.2019.2920008.

• I.R. Lima, G. Cao, and N. Billor, ``M-based simultaneous inference for the mean function of functional data,'' Annals of the Institute of Statistical Mathematics, vol.71, no.3, pp.577-598, June 2019. DOI: 10.1007/s10463-018-0656-y.

• Y. Wang, Y. Shen, S. Mao, X. Chen, and H. Zou, “LASSO & LSTM integrated temporal model for short-term solar intensity forecasting,” IEEE Internet of Things Journal, vol.6, no.2, pp.2933-2944, Apr. 2019. DOI: 10.1109/JIOT.2018.2877510.

• H. Zou, Y. Wang, S. Mao, F. Zhang, and X. Chen, “Online energy management in microgrid considering reactive power,” IEEE Internet of Things Journal, vol.6, no.2, pp.2895-2906, Apr. 2019. DOI: 10.1109/JIOT.2018.2876245.

• N. Tang, S. Mao, Y. Wang, and R.M. Nelms, “Solar power generation forecasting with a LASSO-based approach,” IEEE Internet of Things Journal, vol.5, no.2, pp.1090-1099, Apr. 2018. DOI: 10.1109/JIOT.2018.2812155.

• G. Cao and L. Wang, ``Simultaneous inference for the mean of repeated functional data,'' Journal of Multivariate Analysis, vol.165, pp.279-295, May 2018. DOI: 10.1016/j.jmva.2018.02.001

• Y. Wang, Y. Shen, S. Mao, G. Cao, and R.M. Nelms, “Adaptive learning hybrid model for solar intensity forecasting,” IEEE Transactions on Industrial Informatics, Special Issue on Energy Informatics for Green Cities, vol.14, no.4, pp.1635-1645, Apr. 2018. DOI: 10.1109/TII.2017.2789289.

• Y. Wang, S. Mao, and R.M. Nelms, “On hierarchical power scheduling for the macrogrid and cooperative microgrids,” IEEE Transactions on Industrial Informatics, Special Issue on New Trends of Demand Response in Smart Grid, vol.11, no.6, pp.1574-1584, Dec. 2015. DOI: 10.1109/TII.2015.2417496.

• Y. Huang, S. Mao, and R.M. Nelms, “Smooth scheduling for electricity distribution in the smart grid,” IEEE Systems Journal, vol.9, no.3, pp.966-977, Sept. 2015. DOI: 10.1109/JSYST.2014.2340231

• Y. Wang, S. Mao, and R.M. Nelms, “Asymptotic optimal online energy distribution in the smart grid,” invited paper, E-Letter of the IEEE Communications Society Multimedia Communications Technical Committee (MMTC), Special Issue on Smart Grid, vol. 9, no. 4, pp.33-36, July 2014.

• Y. Wang, S. Mao, and R.M. Nelms, “Distributed online algorithm for optimal real-time energy distribution in the smart grid,” IEEE Internet of Things Journal, vol.1, no.1, pp.70-80, Feb. 2014. DOI: 10.1109/JIOT.2014.2305667.

• Y. Huang, S. Mao, and R.M. Nelms, “Adaptive electricity scheduling in microgrids,” IEEE Transactions on Smart Grid, vol.5, no.1, pp.270-281, Jan. 2014. DOI: 10.1109/TSG.2013.2282823.

• Y. Wang, S. Mao, and R.M. Nelms, “An online algorithm for optimal real-time energy distribution in smart grid,” IEEE Transactions on Emerging Topics in Computing, Special Issue on Cyber-Physical Systems, vol.1, no.1, pp.10-21, July 2013. DOI: 10.1109/TETC.2013.2273218.

• Y. Huang and S. Mao, “On Quality of Usage provisioning for electricity scheduling in microgrids,” IEEE Systems Journal, Special Issue on Smart Grid Communications Systems, vol.8, no.2, pp.619-628, June 2014. DOI: 10.1109/JSYST.2013.2260941.

Related Publications (conference)

• N. Tang, S. Mao, and R. M. Nelms, “Adversarial attacks to solar power forecast,” in Proc. IEEE GLOBECOM 2021, Madrid, Spain, Dec. 2021.

• G. Cao, S. Wang, and L. Wang, “Estimation and inference for functional linear regression models with varying regression coefficients,” in Proc. American Statistical Association's 2019 Joint Statistical Meeting, Denver, CO, July/Aug. 2019.

• L. Wang, S. Mao, and B. Wilamowski, “Short-term load forecasting with LSTM based ensemble learning,” invited paper, in Proc. IEEE GreenCom 2019, Atlanta, GA, July 2019, pp.793-800.

• G. Cao, S. Wang, and L. Wang, “Estimation and inference for functional linear regression models with varying regression coefficients,” in Proc. ICSA 2019 Applied Statistics Symposium, Raleigh, NC, June 2019.

• N. Tang, S. Mao, Y. Wang, and R.M. Nelms, “LASSO-based single index model for solar power generation forecasting,” in Proc. IEEE GLOBECOM 2017, Singapore, Dec. 2017.

• Y. Wang, G. Cao, S. Mao, and R.M. Nelms, “Analysis of solar generation and weather data in smart grid with simultaneous inference of nonlinear time series,” in Proc. IEEE INFOCOM WKSHPS, The 2015 International Workshop on Smart Cities and Urban Informatics (SmartCity 2015), in conjunction with IEEE INFOCOM 2015, Hong Kong, P.R. China, Apr. 2015, pp.672-677.

• Y. Wang, S. Mao, and R.M. Nelms, “Optimal hierarchical power scheduling for cooperative microgrids,” poster paper, in Proc. IEEE MASS 2014, Philadelphia, PA, Oct. 2014, pp.497-498.

• Y. Wang, S. Mao, and R.M. Nelms, “A distributed online algorithm for optimal real-time energy distribution in smart grid,” in Proc. IEEE GLOBECOM 2013, pp.1644-1649, Atlanta, GA, December 2013.

• Y. Huang, S. Mao, and R.M. Nelms, “Adaptive electricity scheduling in microgrids,” in Proc. IEEE INFOCOM 2013, pp.1142-1150, Turin, Italy, April 2013.

• Y. Huang, S. Mao, and R.M. Nelms, “Smooth electric power scheduling in power distribution networks,” in Proc. IEEE GLOBECOM 2012 - Workshop on Smart Grid Communications: Design for Performance, pp.1469-1473, Anaheim, CA, December 2012.

• Y. Huang and S. Mao, “Adaptive electricity scheduling with quality of usage guarantees in microgrids,” in Proc. IEEE GLOBECOM 2012, pp.5160-5165, Anaheim, CA, December 2012.

We acknowledge the generous support from our sponsor

• National Science Foundation

This work is supported in part by the U.S. National Science Foundation (NSF) under Grant DMS-1736470. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the foundation.