Aggregated Modeling and Control of Air Conditioning Loads for Demand Response

Demand response is playing an increasingly important role in the efficient and reliable operation of the electric grid. Modeling the dynamic behavior of a large population of responsive loads is especially important to evaluate the effectiveness of various demand response strategies. In this paper, a highly accurate aggregated model is developed for a population of air conditioning loads. The model effectively includes statistical information of the load population, systematically deals with load heterogeneity, and accounts for second-order dynamics necessary to accurately capture the transient dynamics in the collective response. Based on the model, a novel aggregated control strategy is designed for the load population under realistic conditions. The proposed controller is fully responsive and achieves the control objective without sacrificing end-use performance. The proposed aggregated modeling and control strategy is validated through realistic simulations using GridLAB-D. Extensive simulation results indicate that the proposed approach can effectively manage a large number of air conditioning systems to provide various demand response services, such as frequency regulation and peak load reduction.

[1]  Katsumi Matsuura,et al.  Kinetic performance and energy profile in a roller coaster electron transfer chain: a study of modified tetraheme-reaction center constructs. , 2006, Journal of the American Chemical Society.

[2]  François Bouffard,et al.  Decentralized Demand-Side Contribution to Primary Frequency Control , 2011, IEEE Transactions on Power Systems.

[3]  Shuai Lu,et al.  Development and Validation of Aggregated Models for Thermostatic Controlled Loads with Demand Response , 2012, 2012 45th Hawaii International Conference on System Sciences.

[4]  V. Vittal,et al.  A Framework for Evaluation of Advanced Direct Load Control With Minimum Disruption , 2008, IEEE Transactions on Power Systems.

[5]  Soumya Kundu,et al.  Safe Protocols for Generating Power Pulses with Heterogeneous Populations of Thermostatically Controlled Loads , 2012, 1211.0248.

[6]  Wei Zhang,et al.  Aggregate model for heterogeneous thermostatically controlled loads with demand response , 2012, 2012 IEEE Power and Energy Society General Meeting.

[7]  Sanem Sergici,et al.  The Impact of Informational Feedback on Energy Consumption -- A Survey of the Experimental Evidence , 2009 .

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

[9]  T.O. Ting,et al.  A novel approach for unit commitment problem via an effective hybrid particle swarm optimization , 2006, IEEE Transactions on Power Systems.

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

[11]  S. Borenstein,et al.  Dynamic Pricing, Advanced Metering, and Demand Response in Electricity Markets , 2002 .

[12]  Wen-Chen Chu,et al.  Scheduling of direct load control to minimize load reduction for a utility suffering from generation shortage , 1993 .

[13]  R. Adapa,et al.  Scheduling direct load control to minimize system operation cost , 1995 .

[14]  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.

[15]  U. Topcu,et al.  Fast load control with stochastic frequency measurement , 2012, 2012 IEEE Power and Energy Society General Meeting.

[16]  H. Allcott,et al.  Rethinking Real Time Electricity Pricing , 2011 .

[17]  B. S. Wagner,et al.  Equivalent thermal parameters for an occupied gas-heated house , 1985 .

[18]  D. Brandt,et al.  A linear programming model for reducing system peak through customer load control programs , 1996 .

[19]  William W. Hogan.  Demand response compensation, net Benefits and cost allocation: comments , 2010 .

[20]  H. Chao.  Price-Responsive Demand Management for a Smart Grid World , 2010 .

[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]  Yann-Chang Huang,et al.  A model reference adaptive control strategy for interruptible load management , 2004 .

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

[24]  Jianming Lian,et al.  Reduced-order modeling of aggregated thermostatic loads with demand response , 2012, 2012 IEEE 51st IEEE Conference on Decision and Control (CDC).

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

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

[27]  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.

[28]  S.E. Widergren,et al.  Modeling uncertainties in aggregated thermostatically controlled loads using a State queueing model , 2005, IEEE Transactions on Power Systems.

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