Model analysis of a residential building for demand response

This paper focuses on developing a thermal model in MATLAB that considers both thermal resistance and capacitance in building envelope, using the same assumptions in a building energy simulation tool EnergyPlus to compare the indoor temperature changes between these two, implementing the residential model to find optimal solution for air-conditioning system in smart home energy management systems (SHEMS). The conventional model which is routinely used in the available literature on demand response and home energy management usually neglects the dynamics of a second-order system due to the thermal mass of the house. As a consequence, the energy demand based on the conventional model may be over evaluated. A detailed single family house model was developed using EnergyPlus and the simulation result generated from MATLAB model was compared with the outcome from EnergyPlus. The study shows solar radiation can change the thermal behavior at a noticeable level. Such a less complex model is more amenable to study the effect of thermal inertia in demand response, hence will make the optimization results for scheduling and coordinating distributed energy resources in home energy management more realistic.

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

[2]  Michael Vollmer,et al.  Newton's law of cooling revisited , 2009 .

[3]  E. D. Spooner,et al.  Robust scheduling of residential distributed energy resources using a novel energy service decision-support tool , 2011, ISGT 2011.

[4]  Ross Baldick,et al.  Dynamic Demand Response Controller Based on Real-Time Retail Price for Residential Buildings , 2014, IEEE Transactions on Smart Grid.

[5]  Karl Henrik Johansson,et al.  Scheduling smart home appliances using mixed integer linear programming , 2011, IEEE Conference on Decision and Control and European Control Conference.

[6]  James E. Braun,et al.  An Inverse Gray-Box Model for Transient Building Load Prediction , 2002 .

[7]  Peter Palensky,et al.  Demand Side Management: Demand Response, Intelligent Energy Systems, and Smart Loads , 2011, IEEE Transactions on Industrial Informatics.

[8]  Gregor Verbic,et al.  Towards a smart home energy management system - A dynamic programming approach , 2011, 2011 IEEE PES Innovative Smart Grid Technologies.

[9]  Santiago Grijalva,et al.  Modeling for Residential Electricity Optimization in Dynamic Pricing Environments , 2012, IEEE Transactions on Smart Grid.

[10]  Shengwei Wang,et al.  Parameter estimation of internal thermal mass of building dynamic models using genetic algorithm , 2006 .

[11]  Iain MacGill,et al.  Coordinated Scheduling of Residential Distributed Energy Resources to Optimize Smart Home Energy Services , 2010, IEEE Transactions on Smart Grid.

[12]  S. Grijalva,et al.  Realizing smart grid benefits requires energy optimization algorithms at residential level , 2011, ISGT 2011.

[13]  Gregor Verbic,et al.  Investigating thermal inertia in lightweight buildings for demand response , 2014, 2014 Australasian Universities Power Engineering Conference (AUPEC).

[14]  Carlos F. Pfeiffer,et al.  Modelling the heat dynamics of a residential building unit: Application to Norwegian buildings. , 2014 .

[15]  Pascal Van Hentenryck,et al.  Residential Demand Response under Uncertainty , 2013, CP.

[16]  G. Rizzoni,et al.  A highly resolved modeling technique to simulate residential power demand , 2013 .

[17]  Kankar Bhattacharya,et al.  Optimal Operation of Residential Energy Hubs in Smart Grids , 2012, IEEE Transactions on Smart Grid.

[18]  Wilhelm A. Friess,et al.  Wall insulation measures for residential villas in Dubai: A case study in energy efficiency , 2012 .

[19]  Nelson Fumo,et al.  Methodology to estimate building energy consumption using EnergyPlus Benchmark Models , 2010 .