On the range of feasible power trajectories for a population of thermostatically controlled loads

We study the potential of a population of thermostatically controlled loads to track desired power signals with provable guarantees. Based on connecting the temperature state of an individual device with its internal energy, we derive necessary conditions that a given power signal needs to satisfy in order for the aggregation of devices to track it using non-disruptive probabilistic switching control. Our derivation takes into account hybrid individual dynamics, an accurate continuous-time Markov chain model for the population dynamics and bounds on switching rates of individual devices. We illustrate the approach with case studies.

[1]  Henk A. P. Blom,et al.  Risk Decomposition and Assessment methods , 2003 .

[2]  Duncan S. Callaway,et al.  State Estimation and Control of Electric Loads to Manage Real-Time Energy Imbalance , 2013, IEEE Transactions on Power Systems.

[3]  Duncan S. Callaway,et al.  Arbitraging Intraday Wholesale Energy Market Prices With Aggregations of Thermostatic Loads , 2015, IEEE Transactions on Power Systems.

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

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

[6]  Kiyosi Itô,et al.  Essentials of Stochastic Processes , 2006 .

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

[8]  Tyrone L. Vincent,et al.  Improved battery models of an aggregation of Thermostatically Controlled Loads for frequency regulation , 2014, 2014 American Control Conference.

[9]  J. Horlock,et al.  Engineering Thermodynamics , 1958, Nature.

[10]  Luminita Cristiana Totu,et al.  Demand Response of Thermostatic Loads by Optimized Switching-Fraction Broadcast , 2014 .

[11]  John Lygeros,et al.  Approximation of General Stochastic Hybrid Systems by Switching Diffusions with Random Hybrid Jumps , 2008, HSCC.

[12]  Arman C. Kizilkale,et al.  Mean field based control of power system dispersed energy storage devices for peak load relief , 2013, 52nd IEEE Conference on Decision and Control.

[13]  Dario Bauso,et al.  Mean-Field Games and Dynamic Demand Management in Power Grids , 2014, Dyn. Games Appl..