(一)国际会议组委会情况:
1. 论文出版主席&分会主席,(Publication Chair & Session Chair) 2018 IEEE International Conference on Innovative Smart Grid Technologies (ISGT Asia), May 22-25, 2018, Singapore.
2.专题主席,(Special Session Chair) 2019 IEEE International Conference on Innovative Smart Grid Technologies (ISGT Asia), May 21-24, 2019, Chengdu, China.
3.分会主席,(Session Chair) 2020 IEEE Asia-Pacific Power and Energy Engineering Conference (APPEEC), Sept. 20-23, 2020, Nanjing, China.
4.分会主席,(Session Chair) 2020 IEEE中国超导专业委员会(CCCSC)应用超导学术年会,Dec. 19-21, 2020, Huzhou, China.
5.分会主席,(Session Chair) 2022 IEEE 5th International Electrical and Energy Conference (CIEEC), May. 27-29, 2022, Nanjing, China.
6.专题主席,(Special Session Chair) 2022 International Conference on Cyber-physical Social Intelligence (ICCSI), Oct. 21-24, 2022, Nanjing, China.
(二)IEEE PES组委会和会员情况:
1.组委会成员(Committee Member):2018 IEEE Power & Energy Society (PES) Singapore Chapter, Committee Member (2018 IEEE PES新加坡分会,骨干成员)
2.会员(Member):IEEE Member and IEEE PES Member, 2018年至今
代表性期刊论文:
[1] H. Quan, D. Srinivasan, and A. Khosravi, “Short-term load and wind power forecasting using neural network-based prediction intervals,” IEEE Transactions on Neural Networks and Learning Systems, vol.25, no.2, pp.303-315, Feb. 2014. (ESI 高被引论文)
[2] H. Quan, D. Srinivasan, and A. Khosravi, “Particle swarm optimization for construction of neural network-based prediction intervals,” Neurocomputing, vol. 127, pp. 172-180, Mar. 2014.
[3] H. Quan, D. Srinivasan, and A. Khosravi, “Uncertainty handling using neural network-based prediction intervals for electrical load forecasting,” Energy, vol. 73, pp. 916-925, Aug. 2014.
[4] H. Quan, D. Srinivasan, A. M. Khambadkone and A. Khosravi, “A computational framework for uncertainty integration in stochastic unit commitment with intermittent renewable energy resources,” Applied Energy, vol. 152, pp. 71-82, Aug. 2015.
[5] H. Quan, D. Srinivasan, and A. Khosravi, “Incorporating wind power forecast uncertainties into stochastic unit commitment using neural network-based prediction intervals,” IEEE Transactions on Neural Networks and Learning Systems, vol. 26, no. 9, pp. 2123-2135, Sept. 2015.
[6] H. Quan, D. Srinivasan and A. Khosravi, “Integration of renewable generation uncertainties into stochastic unit commitment considering reserve and risk: a comparative study,” Energy, vol. 103, pp. 735-745, May. 2016.
[7] D. Yang, H. Quan, V. R. Disfanic and L. Liu, “Reconciling solar forecasts: Geographical hierarchy,” Solar Energy, vol.146, pp. 276–286, April 2017.
[8] D. Yang, H. Quan, V. R. Disfanic and Carlos D. Rodríguez-Gallegos, “Reconciling solar forecasts: Temporal hierarchy,” Solar Energy, vol.158, pp. 332–346, Dec. 2017.
[9] W. Zhang, H. Quan and D. Srinivasan, “Parallel and reliable probabilistic load forecasting via quantile regression forest and quantile determination,” Energy, vol.160, pp. 810-819, Oct. 2018.
[10] W. Zhang, O. Gandhi, H. Quan, Carlos D. Rodríguez-Gallegos and D. Srinivasan, “A multi-agent based integrated volt-var optimization engine for fast vehicle-to-grid reactive power dispatch and EV coordination,” Applied Energy, vol.229, pp. 96-110, Nov. 2018.
[11] W. Zhang, H. Quan and D. Srinivasan, “An improved quantile regression neural network for probabilistic load forecasting,” IEEE Transactions on Smart Grid, vol.10, no.4, pp. 4425-4434, Jul. 2019.
[12] H. Quan and D. Yang, “Probabilistic solar irradiance transposition models,” Renewable and Sustainable Energy Reviews, vol.125, pp. 1–10, June. 2020.
[13] H. Quan, A. Khosravi, D. Yang and D. Srinivasan, “A survey of computational intelligence techniques for wind power uncertainty quantification in smart grids,” IEEE Transactions on Neural Networks and Learning Systems, vol.31, no.11, pp. 4582-4599, Nov. 2020.
[14] W. Zhang, H. Quan*, O. Gandhi, D. Srinivasan, R. Rajagopal and C. W. Tan, “Improving probabilistic load forecasting using quantile regression NN with skip connections,” IEEE Transactions on Smart Grid, vol.11, no.6, pp. 5442-5450, Nov. 2020.
[15] W. Zhang, S. Liu, O. Gandhi, C. D. Rodriguez-Gallegos, H. Quan* and D. Srinivasan, "Deep-learning-based probabilistic estimation of solar PV soiling loss," IEEE Transactions on Sustainable Energy, vol. 12, no. 4, pp. 2436-2444, 2021.
[16] D. S. Kumar, H. Quan, K. Y. Wen, & D. Srinivasan, "Probabilistic risk and severity analysis of power systems with high penetration of photovoltaics," Solar Energy, vol. 230, pp. 1156-1164, 2021.
[17] H. Quan, J. Lv, W. Zhang and W. Tao, "Spatial correlation modeling for optimal power flow with wind power: Feasibility in application of superconductivity," IEEE Transactions on Applied Superconductivity, vol. 31, no. 8, pp. 1-5, 2021.
[18] H. Quan, K. Utkarsh and D. Srinivasan, "A distributed dual-optimization framework for ancillary-service coordination between MV microgrids and LV distribution networks," IEEE Systems Journal, doi: 10.1109/JSYST.2022.3159387, 2022.
[19] H. Quan, J. Lv, J. Guo and W. Zhang, “Investigation of spatial correlation on optimal power flow with high penetration of wind power: A comparative study,” Applied Energy, vol. 316, pp. 1-10, June 2022.
[20] 权浩, 吕立臻, 郭健, 葛轶文, 柳伟. 基于自然进化策略的可再生能源ELM预测两阶段优化训练方法, 电力系统自动化, 2022.