2020年亚太电力与能源国际学术会议（APPEEC）最佳论文奖；
2020年IEEE CCCSC应用超导学术年会“最佳青年科学家奖”；
2019年江苏省“双创计划”高校创新类双创博士；
2015年新加坡科技研究局智能电网实验中心电网操作员资历；
2011-2014年新加坡国立大学4年博士全额奖学金。
（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.
（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
代表性项目列表：
1.中国国家自然科学基金，项目号：51907090，2020-2022，主持
2.新加坡国家研究基金和能源市场管理局项目，Advanced Solar Power Forecasting for Safe and Reliable PV Grid Integration in Singapore--PV Inverter Modelling and Performance Testing，2018-2020，主持
3.校自主科研资助项目，项目号：30919011292，2019-2021，主持
4.新加坡电网公司和新加坡科技研究局智能电网实验中心合作项目，Test-bedding of Microgrid，2016-2017，参与
5.新加坡科技研究局研究所间合作项目（ICES-SIMTech），Investigation on Forecasting with Disaggregated PV Data for Grid Integration at Distribution Level，2016-2016，主持
6.新加坡科技研究局智能电网实验中心研究项目，Short-Term Solar Irradiance Forecasting Using EPGC Data，2015-2015，主持
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.