Ancillary Service to the Grid Through Control of Fans in Commercial Building HVAC Systems

The thermal storage potential in commercial buildings is an enormous resource for providing various ancillary services to the grid. In this paper, we show how fans in Heating, Ventilation, and Air Conditioning (HVAC) systems of commercial buildings alone can provide substantial frequency regulation service, with little change in their indoor environments. A feedforward architecture is proposed to control the fan power consumption to track a regulation signal. The proposed control scheme is then tested through simulations based on a calibrated high fidelity non-linear model of a building. Model parameters are identified from data collected in Pugh Hall, a commercial building located on the University of Florida campus. For the HVAC system under consideration, numerical experiments demonstrate how up to 15% of the rated fan power can be deployed for regulation purpose while having little effect on the building indoor temperature. The regulation signal that can be successfully tracked is constrained in the frequency band [1/τ0,1/τ1], where τ0 ≈ 3 minutes and τ1 ≈ 8 seconds. Our results indicate that fans in existing commercial buildings in the U.S. can provide about 70% of the current national regulation reserve requirements in the aforementioned frequency band. A unique advantage of the proposed control scheme is that assessing the value of the ancillary service provided is trivial, which is in stark contrast to many demand-response programs.

[1]  Prashant G. Mehta,et al.  Mean-field control for energy efficient buildings , 2012, 2012 American Control Conference (ACC).

[2]  Johanna L. Mathieu Modeling, Analysis, and Control of Demand Response Resources , 2012 .

[3]  Fred Schweppe,et al.  Homeostatic Utility Control , 1980, IEEE Transactions on Power Apparatus and Systems.

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

[5]  B. Kirby,et al.  Frequency Regulation Basics and Trends , 2005 .

[6]  Sean P. Meyn,et al.  The value of volatile resources in electricity markets , 2010, 49th IEEE Conference on Decision and Control (CDC).

[7]  Prabir Barooah,et al.  Issues in identification of control-oriented thermal models of zones in multi-zone buildings , 2012, 2012 IEEE 51st IEEE Conference on Decision and Control (CDC).

[8]  Duncan S. Callaway Tapping the energy storage potential in electric loads to deliver load following and regulation, with application to wind energy , 2009 .

[9]  Sandia Report,et al.  Energy Storage for the Electricity Grid: Benefits and Market Potential Assessment Guide A Study for the DOE Energy Storage Systems Program , 2010 .

[10]  Sila Kiliccote,et al.  Advanced Controls and Communications for Demand Response and Energy Efficiency in Commercial Buildings , 2006 .

[11]  Sila Kiliccote,et al.  Field Testing of Automated Demand Response for Integration of Renewable Resources in California's Ancillary Services Market for Regulation Products , 2013 .

[12]  Manfred Morari,et al.  Reducing peak electricity demand in building climate control using real-time pricing and model predictive control , 2010, 49th IEEE Conference on Decision and Control (CDC).

[13]  Bo Li,et al.  Economic model predictive control for building energy systems , 2011, ISGT 2011.

[14]  Sila Kiliccote,et al.  Strategies for Demand Response in Commercial Buildings , 2006 .

[15]  James E. Braun,et al.  Reducing energy costs and peak electrical demand through optimal control of building thermal storage , 1990 .

[16]  M. Lynn Hawaii International Conference on System Sciences , 1996 .

[17]  Ralph Masiello,et al.  Benefits of fast-response storage devices for system regulation in ISO markets , 2009, 2009 IEEE Power & Energy Society General Meeting.

[18]  Manfred Morari,et al.  Building control and storage management with dynamic tariffs for shaping demand response , 2011, 2011 2nd IEEE PES International Conference and Exhibition on Innovative Smart Grid Technologies.

[19]  Zhiwei Lian,et al.  Thermal analysis of cooling coils based on a dynamic model , 2004 .

[20]  Li Xiaolei,et al.  Using Dimmable Lighting for Regulation Capacity and Non-Spinning Reserves in the Ancillary Services Market. A Feasibility Study. , 2011 .

[21]  Johanna L. Mathieu,et al.  State Estimation and Control of Heterogeneous Thermostatically Controlled Loads for Load Following , 2012, 2012 45th Hawaii International Conference on System Sciences.

[22]  John Baillieul,et al.  A Two Level Feedback System Design to Regulation Service Provision , 2013, ArXiv.

[23]  Tyrone L. Vincent,et al.  A generalized battery model of a collection of Thermostatically Controlled Loads for providing ancillary service , 2013, 2013 51st Annual Allerton Conference on Communication, Control, and Computing (Allerton).

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

[25]  Jessica Granderson Evaluation of the Predictive Accuracy of Five Whole Building Baseline Models , 2012 .

[26]  D.G. Infield,et al.  Stabilization of Grid Frequency Through Dynamic Demand Control , 2007, IEEE Transactions on Power Systems.

[27]  M. Piette,et al.  Peak Demand Reduction from Pre-Cooling with Zone Temperature Reset in an Office Building , 2004 .

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

[29]  E.A. DeMeo,et al.  Utility Wind Integration and Operating Impact State of the Art , 2007, IEEE Transactions on Power Systems.

[30]  Scott Backhaus,et al.  Modeling and control of thermostatically controlled loads , 2011 .

[31]  David E. Claridge A Perspective on Methods for Analysis of Measured Energy Data from Commercial Buildings , 1998 .