06/2019, Congrats Shu Chen(Online learning-based demand aggregation), Zixin Cui(Using electricity consumption for the correlation analysis between human circadian rhythm and morbidities), and Lingshuang Yu(Using smart meter data for human circadian rhythm detection) on completing their senior designs.
06/2019, I received the research fund from NSF-Jiangsu to study the new residential demand aggregation method(SBK2019043173).
05/2019, I received the research fund from Jiangsu Provincial State Grid Economic Research Institute to study the planning and operation of the integrated energy systems for Jiangsu.
03/2019, I received the fundamental research funds for the central universities (2242019K40017) to study the new demand aggregation method powered by IoT devices.
03/2019, I received the research fund from Global Energy Interconnection Research Insitute to study how demand aggregation can facilitate frequency regulation.
12/2018, I received the state key lab research fund from NARI (the largest electric power equipment vendor in China) to study how the regional electric power grid can benefit from distributed control algorithms.
11/2018, Our paper “Dispatchable Generation of a Novel Compressed-Air Assisted Wind Turbine and its Operation Mechanism” is accepted by IEEE Trans. on Sustainable Energy.
10/2018, I presented our work “Unlocking Underutilized Grid Assets by Learning Techniques” on 2nd IEEE Conference on Energy lnternet and Energy System Integration, North China Electric Power University, Beijing, China
09/2018, I presented our work “The Anomaly Detection of a Demand Response Survey Adopting Local Outlier Factor” and served as session chair on The 7th International Conference on Renewable Power Generation 2018, Copenhagen, Denmark
08/2018, I presented our work “Power System Capacity Expansion under Higher Penetration of Renewables Considering Flexibility Constraints and Low Carbon Policies” on IEEE PES General Meeting 2018 at Portland, OR, United States
03/2018, I presented our work “Unlocking Underutilized Grid Assets by Learning Techniques: A Case Study on Residential Demand Response” on 52nd Conference on Information Sciences and Systems at Princeton University