Model for Aggregated Water Heater Load Using Dynamic Bayesian Networks

The transition to the new generation power grid, or “smart grid”, requires novel ways of using and analyzing data collected from the grid infrastructure. Fundamental functionalities like demand response (DR), that the smart grid needs, rely heavily on the ability of the energy providers and distributors to forecast the load behavior of appliances under different DR strategies. This paper presents a new model of aggregated water heater load, based on dynamic Bayesian networks (DBNs). The model has been validated against simulated data from an open source distribution simulation software (GridLAB-D). The results presented in this paper demonstrate that the DBN model accurately tracks the load profile curves of aggregated water heaters under different testing scenarios.

[1]  Nir Friedman,et al.  Probabilistic Graphical Models - Principles and Techniques , 2009 .

[2]  P. Cappers,et al.  Demand Response in U.S. Electricity Markets: Empirical Evidence , 2010 .

[3]  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.

[4]  Mohammed H. Albadi,et al.  Demand Response in Electricity Markets: An Overview , 2007, 2007 IEEE Power Engineering Society General Meeting.

[5]  Nir Friedman,et al.  Discretizing Continuous Attributes While Learning Bayesian Networks , 1996, ICML.

[6]  Geoffrey Zweig,et al.  Speech Recognition with Dynamic Bayesian Networks , 1998, AAAI/IAAI.

[7]  R. Weron.  Modeling and Forecasting Electricity Loads and Prices: A Statistical Approach , 2006 .

[8]  L. Goel,et al.  Reliability enhancement of a deregulated power system considering demand response , 2006, 2006 IEEE Power Engineering Society General Meeting.

[9]  R. Azami,et al.  Impact of demand response programs on system and nodal reliability of a deregulated power system , 2008, 2008 IEEE International Conference on Sustainable Energy Technologies.

[10]  Olivier Pourret,et al.  Bayesian networks : a practical guide to applications , 2008 .

[11]  Roland P. Malhamé,et al.  A physically-based computer model of aggregate electric water heating loads , 1994 .

[12]  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.

[13]  Andrès Márquez,et al.  Fault Detection in Distributed Climate Sensor Networks Using Dynamic Bayesian Networks , 2010, 2010 IEEE Sixth International Conference on e-Science.

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

[15]  Krishnan Gowri,et al.  GridLAB-D Technical Support Document: Residential End-Use Module Version 1.0 , 2008 .

[16]  Judea Pearl,et al.  Bayesian Networks , 1998, Encyclopedia of Social Network Analysis and Mining. 2nd Ed..

[17]  H. Farhangi,et al.  The path of the smart grid , 2010, IEEE Power and Energy Magazine.

[18]  Khosrow Moslehi,et al.  A Reliability Perspective of the Smart Grid , 2010, IEEE Transactions on Smart Grid.

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

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