Dr Yongning Zhao

2022

• Jin, J., Ye, L., Li, J., Zhao, Y., Lu, P., Wang, W. and Wang, X. 2022. Wind and photovoltaic power time series data aggregation method based on ensemble clustering and Markov chain. CSEE Journal of Power and Energy Systems 8(3), pp. 757 - 768. (10.17775/CSEEJPES.2020.03700)

2021

• Zhao, Y., Xu, X., Qadrdan, M. and Wu, J. 2021. Optimal operation of compressor units in gas networks to provide flexibility to power systems. Applied Energy 290, article number: 116740. (10.1016/j.apenergy.2021.116740)

2020

• Lu, P., Ye, L., Zhong, W., Qu, Y., Zhai, B., Tang, Y. and Zhao, Y. 2020. A novel spatio-temporal wind power forecasting framework based on multi-output support vector machine and optimization strategy. Journal of Cleaner Production 254, article number: 119993. (10.1016/j.jclepro.2020.119993)
• Zhao, Y., Qadrdan, M. and Jenkins, N. 2020. A modelling framework for characterising the impacts of uncertainty on energy systems. Presented at: The 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), The Hague, The Netherlands, 25-28 October 2020.

2019

• Ye, L. et al. 2019. Hierarchical model predictive control strategy based on dynamic active power dispatch for wind power cluster integration. IEEE Transactions on Power Systems 34(6), pp. 4617-4629. (10.1109/TPWRS.2019.2914277)
• Zhao, Y., Ye, L., Wang, Z., Wu, L., Zhai, B., Lan, H. and Yang, S. 2019. Spatio-temporal Markov chain model for very-short-term wind power forecasting. Journal of Engineering 2019(18), pp. 5018-5022. (10.1049/joe.2018.9294)
• Ye, L., Zhang, C., Xue, H., Li, J., Lu, P. and Zhao, Y. 2019. Study of assessment on capability of wind power accommodation in regional power grids. Renewable Energy 133, pp. 647-662. (10.1016/j.renene.2018.10.042)
• Ye, L., Zhang, Y., Zhang, C., Lu, P., Zhao, Y. and He, B. 2019. Combined Gaussian Mixture Model and cumulants for probabilistic power flow calculation of integrated wind power network. Computers and Electrical Engineering 74, pp. 117-129. (10.1016/j.compeleceng.2019.01.010)

2018

• Lu, P., Ye, L., Sun, B., Zhang, C., Zhao, Y. and Teng, J. 2018. A new hybrid prediction method of ultra-short-term wind power forecasting based on EEMD-PE and LSSVM optimized by the GSA. Energies 11(4), pp. -., article number: 697. (10.3390/en11040697)
• Zhao, Y., Ye, L., Pinson, P., Tang, Y. and Lu, P. 2018. Correlation-constrained and sparsity-controlled vector autoregressive model for spatio-temporal wind power forecasting. IEEE Transactions on Power Systems 33(5), pp. 5029-5040. (10.1109/TPWRS.2018.2794450)
• Zhao, Y., Ye, L., Wang, W., Sun, H., Ju, Y. and Tang, Y. 2018. Data-driven correction approach to refine power curve of wind farm under wind curtailment. IEEE Transactions on Sustainable Energy 9(1), pp. 95-105. (10.1109/TSTE.2017.2717021)

2017

• Ye, L., Zhao, Y., Zeng, C. and Zhang, C. 2017. Short-term wind power prediction based on spatial model. Renewable Energy 101, pp. 1067-1074. (10.1016/j.renene.2016.09.069)

2016

• Zhao, Y., Ye, L., Li, Z., Song, X., Lang, Y. and Su, J. 2016. A novel bidirectional mechanism based on time series model for wind power forecasting. Applied Energy 177, pp. 793-803. (10.1016/j.apenergy.2016.03.096)
• Li, Z., Ye, L., Zhao, Y., Song, X., Teng, J. and Jin, J. 2016. Short-term wind power prediction based on extreme learning machine with error correction. Protection and Control of Modern Power Systems 1, article number: 1. (10.1186/s41601-016-0016-y)