Energy-efficient Buildings
Overview:
Buildings account for nearly 70% of the electricity usage in the U.S. To improve the energy efficiency of buildings and the overall power grid, it is critical to intelligently manage various energy demands of buildings and coordinate such management across buildings in the smart grid.
At the building level, a key aspect in improving building energy efficiency is to leverage the scheduling flexibility provided by various energy loads and supplies, including HVAC (heating, ventilation and air conditioning), EV (electric vehicle) charging, datacenter computing loads, battery storage and solar power, etc. Among various energy loads, HVAC system accounts for 50% of the total energy consumption. The thermal flywheel effect allows buildings to provide significant flexibility by temporarily unloading the HVAC systems without immediate impact on occupants. However, the building temperature often exhibits randomized behaviors under incomplete modeling due to various building structure and materials, and disturbances from environment and occupants. Therefore, it is essential to develop a data-driven approach for HVAC control and co-schedule heterogeneous energy demands and supplies in a holistic framework.
At the power grid level, it is important to fully leverage the scheduling flexibility from buildings. Most of the previous works, however, focus on developing price-based or incentive-based demand response (DR) strategies to improve power grid efficiency, in which the building energy management system passively follows load reduction signals from the utilities. Little work has been done to consider integrating the intelligent building energy scheduling process with the electricity market economic dispatch strategy in a holistic framework. To further exploit the huge potential of demand response in improving power system efficiency and facilitate customers' engagement level in electricity market, we propose an innovative demand response scheme based on proactive demand participation from smart buildings.
Framework:
Related Publications:
[1] Tianshu Wei*, Yanzhi Wang and Qi Zhu. Deep Reinforcement Learning for HVAC Control in Smart Buildings. to appear in the 54th ACM/IEEE Design Automation Conference (DAC'17). Austin, TX. Jun. 2017
[2] Tianshu Wei*, Mohammad Atiqul Islam, Shaolei Ren and Qi Zhu. Co-Scheduling of Datacenter and HVAC Loads in Mixed-Use Buildings. 7th IEEE International Green and Sustainable Computing Conference (IGSC'16). Hangzhou, China. Nov. 2016
[3] Tianshu Wei* and Qi Zhu. Co-scheduling of Flexible Energy Loads in Building Clusters. IEEE International Symposium on Circuits and Systems (ISCAS'16) (Invited Paper). Montreal, Canada. May 2016
[4] Tianshu Wei*, Qi Zhu and Nanpeng Yu. Proactive Demand Participation of Smart Buildings in Smart Grid. IEEE Transactions on Computers (TC), Vol. 65, No. 5. May 2016
[5] Tianshu Wei*, Bowen Zheng*, Qi Zhu and Shiyan Hu. Security Analysis of Proactive Participation of Smart Buildings in Smart Grid. 34th IEEE/ACM International Conference on Computer-Aided Design (ICCAD'15). Austin, TX. Nov. 2015
[6] Tianshu Wei* and Qi Zhu. Proactive demand participation of heterogeneous flexible loads in smart grid. 6th International Green and Sustainable Computing Conference (IGSC'15). Las Vegas, NV. Dec. 2015
[7] Nanpeng Yu, Tianshu Wei* and Qi Zhu. From Passive Demand Response to Proactive Demand Participation. 11th IEEE International Conference on Automation Science and Engineering (CASE'15). Gothenburg, Sweden. Aug. 2015
[8] Tianshu Wei*, Qi Zhu and Mehdi Maasoumy. Co-scheduling of HVAC Control, EV Charging and Battery Usage for Building Energy Efficiency. 33rd IEEE/ACM International Conference on Computer-Aided Design (ICCAD'14). San Jose, CA. Nov. 2014
[9] Tianshu Wei*, Taeyoung Kim, Sangyoung Parky, Qi Zhu, Sheldon X.-D. Tan, Naehyuck Changy, Sadrul Ula and Mehdi Maasoumy. Battery Management and Application for Energy-Efficient Buildings. 51st IEEE/ACM Design Automation Conference (DAC'14). San Francisco, CA. Jun. 2014