Development and Validation of Aggregated Models for Thermostatic Controlled Loads with Demand Response

One of the salient features of the smart grid is the wide spread use of distributed energy resources (DERs) like small wind turbines, photovoltaic (PV) panels, energy storage (batteries, flywheels, etc), Plug-in Hybrid Electric Vehicles (PHEVs) and controllable end-use loads. The affect of these distributed resources on the distribution feeder and on transmission system operations needs to be understood. Due to the potentially large number of DERs that are expected to be deployed, it is impractical to use detailed models of these resources when integrated with the transmission system. This paper focuses on developing aggregated models for a population of Thermostatic Controlled Loads (TCLs) which are a class of controllable end-use loads. The developed reduced-order models are validated against simulations of thousands of detailed building models using an open source distribution simulation software (Grid LAB-D) under both steady state and dynamic conditions (thermostat setback program as a simple form of demand response).

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

[2]  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.

[3]  Ian A. Hiskens,et al.  Achieving Controllability of Electric Loads This paper discusses actively involving highly distributed loads in power system control actions; an overview of system control objectives is provided. , 2011 .

[4]  Olof M. Jarvegren,et al.  Pacific Northwest GridWise™ Testbed Demonstration Projects; Part I. Olympic Peninsula Project , 2008 .

[5]  Ying Xing,et al.  Load Management Techniques for Distributed Stream Processing , 2006 .

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

[7]  Karanjit Kalsi,et al.  Aggregated modeling of thermostatic loads in demand response: A systems and control perspective , 2011, IEEE Conference on Decision and Control and European Control Conference.

[8]  Sanem Sergici,et al.  Household Response to Dynamic Pricing of Electricity - A Survey of the Empirical Evidence , 2010 .

[9]  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.

[10]  R. C. Sonderegger.  Dynamic models of house heating based on equivalent thermal parameters , 1978 .

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

[12]  D. P. Chassin,et al.  Multi-State Load Models for Distribution System Analysis , 2011, IEEE Transactions on Power Systems.

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