Dynamic frequency regulation resources of commercial buildings through combined building system resources using a supervisory control methodology

Abstract Frequency regulation (FR) is the electric grid service responsible for maintaining the system frequency at its nominal value of 60 Hz in the United States — an indicator of energy balance on the grid. In cases of mismatch between power supply and demand, FR resources either on the generation or the demand side, responding rapidly to restore system frequency to its nominal value. Due to the limited responsiveness of generators, fast and accurate demand side resources (DSR) have recently been encouraged to participate in FR. However, the tested DSRs typically require high initial equipment investment (e.g., flywheels and batteries). Large commercial buildings can provide effective load shaping with little impact to occupants’ comfort and have significant amount of available capacity for FR participation. In addition, commercial buildings are characterized by numerous interdependent HVAC subsystems and control systems. Therefore, a high-level supervisory control strategy is needed that directs the interdependent HVAC systems for FR with strengthened interactions and depressed counteractions. Simulation results suggest that large commercial buildings can provide significant FR capacity and high performance scores. Dynamic building-to-grid integration automatically and continuously provides solutions maintaining energy balance on the gird. The benefit to the power system reliability would be significant.

[1]  Joseph H. Eto,et al.  Demand Response Spinning Reserve Demonstration , 2007 .

[2]  Peng Zhao,et al.  Evaluation of commercial building HVAC systems as frequency regulation providers , 2013 .

[3]  Gregor P. Henze,et al.  A model predictive control optimization environment for real-time commercial building application , 2013 .

[4]  Johanna L. Mathieu,et al.  Quantifying Changes in Building Electricity Use, With Application to Demand Response , 2011, IEEE Transactions on Smart Grid.

[5]  Todd M. Ryan,et al.  Integration of flywheel-based energy storage for frequency regulation in deregulated markets , 2010, IEEE PES General Meeting.

[6]  Jerald D. Parker,et al.  Heating, Ventilating, and Air Conditioning: Analysis and Design , 1977 .

[7]  Vincent J. Cushing,et al.  Optimizing commercial building participation in energy and ancillary service markets , 2014 .

[8]  J. Braun,et al.  Experimental and simulated performance of optimal control of building thermal storage , 1994 .

[9]  Gregor P. Henze,et al.  Evaluation of optimal control for active and passive building thermal storage , 2004 .

[10]  Standard Ashrae Thermal Environmental Conditions for Human Occupancy , 1992 .

[11]  Arindam Ghosh,et al.  Renewable energy sources and frequency regulation : survey and new perspectives , 2010 .

[12]  Yuri V. Makarov,et al.  Assessing the Value of Regulation Resources Based on Their Time Response Characteristics , 2008 .

[13]  J. Braun,et al.  Load Control Using Building Thermal Mass , 2003 .

[14]  Chet Sandberg,et al.  The Role of Energy Storage in Development of Smart Grids , 2011, Proceedings of the IEEE.

[15]  Howard F. Illian Frequency Control Performance Measurement and Requirements , 2011 .

[16]  Frank Barnes,et al.  Frequency regulation and economic dispatch using integrated storage in a hybrid renewable grid , 2011, 2011 International Conference on Energy, Automation and Signal.

[17]  Refrigerating ASHRAE handbook of fundamentals , 1967 .

[18]  Jian Ma,et al.  Operational Impacts of Wind Generation on California Power Systems , 2009, IEEE Transactions on Power Systems.

[19]  Bimal K. Bose,et al.  Modern Power Electronics and AC Drives , 2001 .

[20]  Sean P. Meyn,et al.  Ancillary service for the grid via control of commercial building HVAC systems , 2013, 2013 American Control Conference.

[21]  Roland B. Stull,et al.  Wet-Bulb Temperature from Relative Humidity and Air Temperature , 2011 .

[22]  Leon M. Tolbert,et al.  Simulink implementation of induction machine model - a modular approach , 2003, IEEE International Electric Machines and Drives Conference, 2003. IEMDC'03..