Safe Protocols for Generating Power Pulses with Heterogeneous Populations of Thermostatically Controlled Loads

We explore methods to use thermostatically controlled loads (TCLs), such as water heaters and air conditioners, to provide ancillary services by assisting in balancing generation and load. We show that by adding simple imbedded instructions and a small amount of memory to temperature controllers of TCLs, it is possible to design open-loop control algorithms capable of creating short-term pulses of demand response without unwanted power oscillations associated with temporary synchronization of the TCL dynamics. By moving a small amount of intelligence to each of the end point TCL devices, we are able to leverage our knowledge of the time dynamics of TCLs to shape the demand response pulses for different power system applications. A significant benefit of our open-loop method is the reduction from two-way to one-way broadcast communication which also eliminates many basic consumer privacy issues. In this work, we focus on developing the algorithms to generate a set of fundamental pulse shapes that can subsequently be used to create demand response with arbitrary profiles. Demand response control methods, such as the one developed here, open the door to fast, nonperturbative control of large aggregations of TCLs.

[1]  Zhao Xu,et al.  Demand as Frequency Controlled Reserve , 2011, IEEE Transactions on Power Systems.

[2]  Ian A. Hiskens,et al.  Achieving Controllability of Electric Loads , 2011, Proceedings of the IEEE.

[3]  A. Debs,et al.  Statistical synthesis of power system functional load models , 1979, 1979 18th IEEE Conference on Decision and Control including the Symposium on Adaptive Processes.

[4]  Refrigerating.  ASHRAE handbook of fundamentals , 1967 .

[5]  K. Schisler,et al.  The role of demand response in ancillary services markets , 2008, 2008 IEEE/PES Transmission and Distribution Conference and Exposition.

[6]  David M. Auslander,et al.  ROBUST CONTROL OF RESIDENTIAL DEMAND RESPONSE NETWORK WITH LOW BANDWIDTH INPUT , 2008 .

[7]  S. El-Ferik,et al.  Identification of physically based models of residential air-conditioners for direct load control management , 2004, 2004 5th Asian Control Conference (IEEE Cat. No.04EX904).

[8]  Guy Desaulniers,et al.  A column generation method for optimal load management via control of electric water heaters , 1995 .

[9]  Mustafa Bagriyanik,et al.  Demand Side Management by controlling refrigerators and its effects on consumers , 2012 .

[10]  B.J. Kirby.  Load Response Fundamentally Matches Power System Reliability Requirements , 2007, 2007 IEEE Power Engineering Society General Meeting.

[11]  Roland Boutros Pierre Malhami.  A statistical approach for modeling a class of power system loads , 1983 .

[12]  R. Pratt,et al.  Use of Residential Smart Appliances for Peak-Load Shifting and Spinning Reserves Cost/Benefit Analysis , 2010 .

[13]  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.

[14]  J. A. Fuentes,et al.  Probabilistic Characterization of Thermostatically Controlled Loads to Model the Impact of Demand Response Programs , 2011, IEEE Transactions on Power Systems.

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

[16]  Refrigerating.  ASHRAE handbook and product directory /published by the American Society of Heating, Refrigerating and Air-Conditioning Engineers, Inc , 1977 .

[17]  N. Lu,et al.  Control strategies of thermostatically controlled appliances in a competitive electricity market , 2005, IEEE Power Engineering Society General Meeting, 2005.

[18]  M. Klobasa.  Analysis of demand response and wind integration in Germany's electricity market , 2010 .

[19]  M. Kintner-Meyer,et al.  Loads Providing Ancillary Services: Review of International Experience , 2008 .

[20]  M. M. Adibi.  Physically Based Modeling of Cold Load Pickup , 2000 .

[21]  J. Oyarzabal,et al.  A Direct Load Control Model for Virtual Power Plant Management , 2009, IEEE Transactions on Power Systems.

[22]  B. Kirby,et al.  Spinning Reserve from Hotel Load Response: Initial Progress , 2008 .

[23]  Chee-yee Chong,et al.  Statistical Synthesis of Physically Based Load Models with Applications to Cold Load Pickup , 1984, IEEE Transactions on Power Apparatus and Systems.

[24]  Fred Schweppe,et al.  Physically Based Modeling of Cold Load Pickup , 1981, IEEE Transactions on Power Apparatus and Systems.

[25]  Manuel Alcázar Ortega,et al.  Technical and economical tools to assess customer demand response in the commercial sector , 2009 .

[26]  Soumya Kundu,et al.  Safe protocol for controlling power consumption by a heterogeneous population of loads , 2012, 2012 American Control Conference (ACC).

[27]  R. Malhamé,et al.  Electric load model synthesis by diffusion approximation of a high-order hybrid-state stochastic system , 1985 .

[28]  Hosam K. Fathy,et al.  Modeling and control insights into demand-side energy management through setpoint control of thermostatic loads , 2011, Proceedings of the 2011 American Control Conference.

[29]  R. E. Mortensen,et al.  A stochastic computer model for heating and cooling loads , 1988 .

[30]  Ernesto Kofman,et al.  Load management: Model-based control of aggregate power for populations of thermostatically controlled loads , 2012 .

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

[32]  Johanna L. Mathieu,et al.  Modeling and Control of Aggregated Heterogeneous Thermostatically Controlled Loads for Ancillary Services , 2011 .

[33]  Goran Strbac,et al.  Demand side management: Benefits and challenges ☆ , 2008 .

[34]  B. J. Kirby,et al.  Spinning Reserve From Responsive Loads , 2003 .