Intelligent Residential Air-Conditioning System With Smart-Grid Functionality

This paper sets forth a novel intelligent residential air-conditioning (A/C) system controller that has smart grid functionality. The qualifier “intelligent” means the A/C system has advanced computational capabilities and uses an array of environmental and occupancy parameters in order to provide optimal intertemporal comfort/cost trade-offs for the resident, conditional on anticipated retail energy prices. The term “smart-grid functionality” means that retail energy prices can depend on wholesale energy prices. Simulation studies are used to demonstrate the capabilities of the proposed A/C system controller.

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

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

[3]  S. Ari,et al.  Constrained fuzzy logic approximation for indoor comfort and energy optimization , 2005, NAFIPS 2005 - 2005 Annual Meeting of the North American Fuzzy Information Processing Society.

[4]  Hossein Salehfar,et al.  Modeling and Evaluation of the System Reliability Effects of Direct Load Control , 1989, IEEE Power Engineering Review.

[5]  Panos Constantopoulos,et al.  Estia: A real-time consumer control scheme for space conditioning usage under spot electricity pricing , 1991, Comput. Oper. Res..

[6]  Yulei Luo,et al.  Mathematical Economics , 2019, Springer Texts in Business and Economics.

[7]  Panos J. Antsaklis,et al.  Linear Systems , 1997 .

[8]  Daniel James Livengood,et al.  The Energy Box : comparing locally automated control strategies of residential electricity consumption under uncertainty , 2011 .

[9]  Ronald G. Harley,et al.  Model Predictive and Genetic Algorithm-Based Optimization of Residential Temperature Control in the Presence of Time-Varying Electricity Prices , 2011, IEEE Transactions on Industry Applications.

[10]  Standard Ashrae.  Thermal Environmental Conditions for Human Occupancy , 1992 .

[11]  E. Stanley Lee,et al.  Fuzzy adaptive networks in thermal comfort , 2006, Appl. Math. Lett..

[12]  Refrigerating.  ASHRAE handbook of fundamentals , 1967 .

[13]  Wolfgang Kastner,et al.  Thermal Comfort Support Application for Smart Home Control , 2012, ISAmI.

[14]  D. Kosterev,et al.  Load modeling in power system studies: WECC progress update , 2008, 2008 IEEE Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century.

[15]  D. Chassin,et al.  A Taxonomy of North American radial distribution feeders , 2009, 2009 IEEE Power & Energy Society General Meeting.

[16]  David P. Chassin,et al.  Residential energy resource models for distribution feeder simulation , 2003, 2003 IEEE Power Engineering Society General Meeting (IEEE Cat. No.03CH37491).

[17]  David E. Culler,et al.  Reducing Transient and Steady State Electricity Consumption in HVAC Using Learning-Based Model-Predictive Control , 2012, Proceedings of the IEEE.

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

[19]  Gianfranco Rizzo,et al.  The control of indoor thermal comfort conditions: introducing a fuzzy adaptive controller , 2004 .

[20]  Ning Lu,et al.  An Evaluation of the HVAC Load Potential for Providing Load Balancing Service , 2012, IEEE Transactions on Smart Grid.

[21]  Vincent W. S. Wong,et al.  Advanced Demand Side Management for the Future Smart Grid Using Mechanism Design , 2012, IEEE Transactions on Smart Grid.

[22]  Yuan-Yih Hsu,et al.  Dispatch of direct load control using dynamic programming , 1991 .

[23]  A. Faruqui,et al.  Household response to dynamic pricing of electricity: a survey of 15 experiments , 2010 .

[24]  D. Chassin,et al.  Analysis of distribution level residential demand response , 2011, 2011 IEEE/PES Power Systems Conference and Exposition.

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

[26]  G. Lachiver,et al.  A fuzzy control system based on the human sensation of thermal comfort , 1998, 1998 IEEE International Conference on Fuzzy Systems Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36228).

[27]  D. Chassin,et al.  Analysis of Residential Demand Response and double-auction markets , 2011, 2011 IEEE Power and Energy Society General Meeting.

[28]  F. Alvarado,et al.  Designing incentive compatible contracts for effective demand management , 2000 .

[29]  Nicholas R. Jennings,et al.  Adaptive home heating control through Gaussian process prediction and mathematical programming , 2011 .

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

[31]  Zhi Zhou,et al.  Agent-Based Electricity Market Simulation With Demand Response From Commercial Buildings , 2011, IEEE Transactions on Smart Grid.

[32]  C.W. Gellings,et al.  The concept of demand-side management for electric utilities , 1985, Proceedings of the IEEE.

[33]  Ruxu Du,et al.  Thermal comfort control based on neural network for HVAC application , 2005, Proceedings of 2005 IEEE Conference on Control Applications, 2005. CCA 2005..

[34]  H. Salehfar,et al.  A production costing methodology for evaluation of direct load control , 1991, IEEE Power Engineering Review.

[35]  Arnaud G. Malan,et al.  HVAC control strategies to enhance comfort and minimise energy usage , 2001 .

[36]  Christian Posse,et al.  Modeling Power Systems as Complex Adaptive Systems , 2004 .

[37]  L. Tesfatsion,et al.  Effects of price-responsive residential demand on retail and wholesale power market operations , 2012, 2012 IEEE Power and Energy Society General Meeting.

[38]  Anastasios I. Dounis,et al.  Advanced control systems engineering for energy and comfort management in a building environment--A review , 2009 .

[39]  R. Sabourin,et al.  Evolutionary algorithms for multi-objective optimization in HVAC system control strategy , 2004, IEEE Annual Meeting of the Fuzzy Information, 2004. Processing NAFIPS '04..

[40]  K. P. Schneider,et al.  Detailed end use load modeling for distribution system analysis , 2010, IEEE PES General Meeting.