2018.09～   至今     南京理工大学，自动化学院电气工程系，教授；

2014.10～2018.08   新加坡科技研究局，智能电网实验中心，研究科学家。

2011.01～2014.08   新加坡国立大学 电气与计算机工程，工学博士；

2008.09～2010.12   华中科技大学 水利水电工程，工学硕士；

2004.09～2008.07   华中科技大学 水利水电工程，工学学士。

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“省级人才计划”称号；

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.

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

代表性项目列表：

1. 电网公司重点专项科技项目，基于数字孪生的变压器安全预警关键技术研究及装置试制， 2022-2024，主持

2. 中国国家自然科学基金，项目号：51907090，2020-2022，主持

4. 江苏省自然科学基金，面上项目，项目号：BK20211195，2021-2024，主持

5. 中央高校基本科研业务费项目，项目号：30919011292，2019-2021，主持

6. 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

7. 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

8. 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.

[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.