Projects

1. 2019-present: Bring energy storage and renewable energy to energy systems via market design, supported by Building Efficiency and Sustainability in the Tropics (NRF of Singapore) [View more]
   
   

   • Designed an optimal and fair sharing mechanism for community energy storage based on coalition game theory which could facilitate the deployment of capital-intensive energy storage techniques in energy systems.

   • Developed a cloud energy storage model to enable economic penetration of volatile renewable generation based on non-cooperative game theory, which could work as an example how market mechanism can shape the efficiency of energy systems.

   • Design efficient and scalable peer-to-peer energy trading market via coalition formation based on cooperative and non-cooperative game theory (in progress).

2. 2018-2019: Adaptive and scalable control of air-conditioning and mechanical ventilation (ACMV) systems, supported by Building Efficiency and Sustainability in the Tropics (NRF of Singapore) [View more]
   
   

   • Developed an adaptive and scalable control method for commercial ACMV systems to achieve high energy efficiency and human comfort (i.e, thermal comfort and indoor air quality) based on reinforcement learning (RL) and decentralized optimization techniques.

   • Proposed a generalized decentralized optimization method for structured non-convex and nonlinear problems which could be applied to address the computational challenges of various complex networked systems.

3. 2015-2018: Scalable coordination of electric vehicle (EV) charging in microgrid of buildings, support by National Natural Science Foundation of China
   
   

   • Data-driven analysis of the stochastic EV charging behaviors and roof-top wind power generation in cities.

   • Established a distributed algorithm to learn the scheduling policies of EV charging in buildings based on reinforcement learning (RL).

   • Developed a decentralized method for the coordination of EV charging with distributed renewable energy in microgrids of buildings.

4. 2014-2015: Data-driven analysis of energy consumption behaviors of Occupants in buildings, support by National Natural Science Foundation of China
   
   

   • Built an experimental platform for collecting the location-based energy consumption data of individual occupants in buildings.

   • Improved the prediction accuracy of occupant’s energy consumption via a data-driven model triggered by multi-dimensional information.