A Novel Model of Intelligent Electrical Load Management by Goal Programming for Smart Houses, Respecting Consumer Preferences

Abstract

Energy management is being highly regarded throughout the world. High-energy consumption in residential buildings is one of the dominant reasons of excessive energy consumption. There are many recent works on the demand-side management (DSM) and smart homes to keep control on electricity consumption. The paper is an intelligence to modify patterns, by proposing a time scheduling consumers, such that they can maintain their welfare while saving benefits from time varying tariffs; a model of household loads is proposed; constraints, including daily energy requirements and consumer preferences are considered in the framework, and the model is solved using mixed integer linear programming. The model is developed for three scenarios, and the results are compared: the 1st scenario aims Peak Shaving; the 2nd minimizes Electricity Cost, and the 3rd one, which distinguishes this study from the other related works, is a combination of the 1st and 2nd Scenarios. Goal programming is applied to solve the 3rd scenario. Finally, the best schedules for household loads are presented by analyzing power distribution curves and comparing results obtained by these scenarios. It is shown that for the case study of this paper with the implementation of 3rd scenario, it is possible to gain 7% saving in the electricity cost without any increasing in the lowest peak power consumption.

Share and Cite:

A. Dehnad and H. Shakouri, "A Novel Model of Intelligent Electrical Load Management by Goal Programming for Smart Houses, Respecting Consumer Preferences," Energy and Power Engineering, Vol. 5 No. 10, 2013, pp. 622-627. doi: 10.4236/epe.2013.510068.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1] R. F. Li, et al., “Smart Transmission Grid: Vision and Framework,” IEEE Transactions on Smart Grid, Vol. 1, No. 2, 2010, pp. 168-177.
[2] C. A. Mohsenian-Rad, V. Wong, J. Jatskevich, R. Schober and A. LeonGarcia, “Autonomous Demand-Side Management Based on Game Theoretic Energy Consumption Scheduling for the Future Smart Grid,” IEEE Transactions on Smart Grid, Vol. 1, No. 3, 2010, pp. 320-331.
[3] M. H. Albadi and E. F. El-Saadany, “Demand Response in Electricity Markets: An Overview,” IEEE Power Engineering Society General Meeting, 2007.
[4] T. J. Lui, W. Stirling and H. O. Marcy, “Get Smart,” IEEE Power Energy Magazine, Vol. 8, 2010, pp. 66-78.
http://dx.doi.org/10.1109/MPE.2010.936353
[5] Pyrko, “Load Demand Pricing—Case Studies in Residential Buildings,” International Energy Effciency in Domestic Appliances and Lighting Conference, 2006.
[6] T. Y. Wu, S. S. Shieh, S. S. Jang and C. C. L. Liu, “Optimal Energy Management Integration for a Petrochemical Plant under Considerations of Uncertain Power Supplies,” IEEE Transactions on Power Systems, Vol. 20, 2005, pp. 1431-1439.
http://dx.doi.org/10.1109/TPWRS.2005.852063
[7] M. H. Nehrir, B. J. LaMeres and V. Gerez, “A Customer-Interactive Electric Water Heater Demand-Side Management Strategy Using Fuzzy Logic,” IEEE Power Engineering Society 1999 Winter Meeting, Vol. 1, 1999, pp. 433-436.
http://dx.doi.org/10.1109/PESW.1999.747494
[8] K. Wacks, “Utility Load Management Using Home Automation,” IEEE Transactions on Consumer Electronics, Vol. 37, 1991, pp. 168-174.
http://dx.doi.org/10.1109/30.79325
[9] S. Tompros, N. Mouratidis, M. Draaijer, A. Foglar and H. Hrasnica, “Enabling Applicability of Energy Saving Applications on the Appliances of the Home Environment,” IEEE Network, Vol. 23, No. 6, 2009, pp. 8-16.
http://dx.doi.org/10.1109/MNET.2009.5350347
[10] Z. Zhu, J. Tang, S. Lambotharan, W. H. Chin and Z. Fan, “An Integer Linear Programming Based Optimization for Home Demand-side Management in Smart Grid,” IEEE PES Innovative Smart Grid Technologies, 2012.
[11] D. ONeill, M. Levorato, A. Goldsmith and U. Mitra, “Residential Demand Response Using Reinforcement Learning,” IEEE Smart Grid-Comm, 2010.
[12] L. Hernández, et al., “Improved Short-Term Load Forecasting Based on Two-Stage Predictions with Artificial Neural Networks in a Microgrid Environment,” Energies, Vol. 6, No. 9, 2013, pp. 4489-4507.
http://dx.doi.org/10.3390/en6094489
[13] Y. Pochet and L. A. Wolsey, “Production Planning by Mixed Integer Programming,” Springer, New York, 2006.
[14] M. J. Schniederjans, “Goal Programming,” Methodology and Applications, Kluwer, Boston, 1995.

Copyright © 2023 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.