State Estimation and Control of Electric Loads to Manage Real-Time Energy Imbalance

This paper explores methods to coordinate aggregations of thermostatically controlled loads (TCLs; including air conditioners and refrigerators) to manage frequency and energy imbalances in power systems. We focus on opportunities to centrally control loads with high accuracy but low requirements for sensing and communications infrastructure. We compare cases when measured load state information (e.g., power consumption and temperature) is 1) available in real time; 2) available, but not in real time; and 3) not available. We use Markov chain models to describe the temperature state evolution of populations of TCLs, and Kalman filtering for both state and joint parameter/state estimation. A look-ahead proportional controller broadcasts control signals to all TCLs, which always remain in their temperature dead-band. Simulations indicate that it is possible to achieve power tracking RMS errors in the range of 0.26%-9.3% of steady state aggregated power consumption. We also report results in terms of the generator compliance threshold which is commonly used in industry. Results depend upon the information available for system identification, state estimation, and control. Depending upon the performance required, TCLs may not need to provide state information to the central controller in real time or at all.

[1]  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.

[2]  D. Simon Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches , 2006 .

[3]  N. Navid-Azarbaijani,et al.  Realizing load reduction functions by aperiodic switching of load groups , 1996 .

[4]  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).

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

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

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

[8]  Leehter Yao,et al.  An iterative deepening genetic algorithm for scheduling of direct load control , 2005 .

[9]  J.M. Mauricio,et al.  Frequency Regulation Contribution Through Variable-Speed Wind Energy Conversion Systems , 2009, IEEE Transactions on Power Systems.

[10]  Serge Lefebvre,et al.  Residential load modeling for predicting distribution transformer load behavior, feeder load and cold load pickup , 2002 .

[11]  A. Molina,et al.  Implementation and assessment of physically based electrical load models: Application to direct load control residential programmes , 2003 .

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

[13]  Jhi-Young Joo,et al.  A possible engineering and economic framework for implementing demand side participation in frequency regulation at value , 2011, 2011 IEEE Power and Energy Society General Meeting.

[14]  Kun-Yuan Huang,et al.  A model reference adaptive control strategy for interruptible load management , 2004, IEEE Transactions on Power Systems.

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

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

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

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

[19]  Ning Lu,et al.  Appliance Commitment for Household Load Scheduling , 2011, IEEE Transactions on Smart Grid.

[20]  J. D. Balcomb,et al.  Buildings in a Test Tube: Validation of the Short-Term Energy Monitoring (STEM) Method (Preprint) , 2001 .

[21]  Olimpo Anaya-Lara,et al.  Contribution of DFIG-based wind farms to power system short-term frequency regulation , 2006 .

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

[23]  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 .

[24]  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.

[25]  N. Lu,et al.  A state-queueing model of thermostatically controlled appliances , 2004 .

[26]  F. Albertini,et al.  Remarks on the observability of nonlinear discrete time systems , 1996 .

[27]  Sila Kiliccote,et al.  Statistical analysis of baseline load models for non-residential buildings , 2009 .

[28]  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.