Application of a Multi-Objective User-Centric Method to Energy Management in Smart Home Considering Consumer Privacy

Document Type : Original Article

Authors

1 MSc, Department of electrical engineering, Yadegar-e-Imam Khomeini(rah) Shahre-Rey Branch, Islamic Azad University, Tehran, Iran

2 Assistant professor, Department of electrical engineering, Yadegar-e-Imam Khomeini(rah) Shahre-Rey Branch, Islamic Azad University, Tehran, Iran

Abstract

This paper investigates smart home energy management in consideration of tradeoffs between residential privacy and energy costs. A multi-objective approach that minimizes energy costs and maximizes privacy protection is proposed. This approach leads to a multi-objective optimization problem in which the two objectives addressed in separate dimensions. PSO algorithm that employs a stochastic search used for power scheduling of home appliances and uses deterministic battery control developed accordingly. The proposed approach can avoid some drawbacks faced by conventional weighted-sum methods for multi-objective optimization. Simulations reveal that the proposed approach can maintain a reasonable energy cost while robustly preserving user privacy at a sensible level, finally the numerical results shown and analyzed.

Keywords

References

[1]S. Lee, B. Kwon, and S. Lee, "Joint energy management system of electric supply and demand in houses and buildings," IEEE Transactions on Power Systems, vol. 29, pp. 2804-2812, 2014.
[2]H. T. Nguyen, D. T. Nguyen, and L. B. Le, "Energy management for households with solar assisted thermal load considering renewable energy and price uncertainty," IEEE Transactions on Smart Grid, vol. 6, pp. 301-314, 2015.
[3]   H.-C. Sun and Y.-C. Huang, "Optimization of power scheduling for energy management in smart homes," Procedia engineering, vol. 38, pp. 1822-1827, 2012.
[4]   M. Braham, T. Miller, A. E. Duerr, M. Lanzone, A. Fesnock, L. LaPre, et al., "Home in the heat: dramatic seasonal variation in home range of desert golden eagles informs management for renewable energy development," Biological Conservation, vol. 186, pp. 225-232, 2015.
[5]   M. Santamouris, C. Cartalis, A. Synnefa, and D. Kolokotsa, "On the impact of urban heat island and global warming on the power demand and electricity consumption of buildings—A review," Energy and Buildings, vol. 98, pp. 119-124, 2015.
[6]   A. B. Haney, T. Jamasb, L. M. Platchkov, and M. G. Pollitt, "Demand-side management strategies and the residential sector: lessons from international experience," The Future of Electricity Demand: Customers, Citizens and Loads, 2010.
[7]   P. Du and N. Lu, "Appliance commitment for household load scheduling," IEEE transactions on Smart Grid, vol. 2, pp. 411-419, 2011.
[8]   K. Clement-Nyns, E. Haesen, and J. Driesen, "The impact of charging plug-in hybrid electric vehicles on a residential distribution grid," IEEE Transactions on power systems, vol. 25, pp. 371-380, 2010.
[9]   X. Guan, Z. Xu, and Q.-S. Jia, "Energy-efficient buildings facilitated by microgrid," IEEE Transactions on smart grid, vol. 1, pp. 243-252, 2010.
[10]N. Batista, R. Melício, J. Matias, and J. Catalão, "Photovoltaic and wind energy systems monitoring and building/home energy management using ZigBee devices within a smart grid," Energy, vol. 49, pp. 306-315, 2013.
[11]K. M. Tsui and S.-C. Chan, "Demand response optimization for smart home scheduling under real-time pricing," IEEE Transactions on Smart Grid, vol. 3, pp. 1812-1821, 2012.
[12]Z. Hong, P. Li, and W. Jingxiao, "Context-aware scheduling algorithm in smart home system," China Communications, vol. 10, pp. 155-164, 2013.
[13]M. A. A. Pedrasa, T. D. Spooner, and I. F. MacGill, "Coordinated scheduling of residential distributed energy resources to optimize smart home energy services," IEEE Transactions on Smart Grid, vol. 1, pp. 134-143, 2010.
[14]M. H. K. Tushar, C. Assi, M. Maier, and M. F. Uddin, "Smart microgrids: Optimal joint scheduling for electric vehicles and home appliances," IEEE Transactions on Smart Grid, vol. 5, pp. 239-250, 2014.
[15]A. Sleman and R. Moeller, "SOA distributed operating system for managing embedded devices in home and building automation," IEEE Transactions on Consumer Electronics, vol. 57, 2011.
[16]Z. Zhao, W. C. Lee, Y. Shin, and K.-B. Song, "An optimal power scheduling method for demand response in home energy management system," IEEE Transactions on Smart Grid, vol. 4, pp. 1391-1400, 2013.
[17]Y.-Y. Hong, J.-K. Lin, C.-P. Wu, and C.-C. Chuang, "Multi-objective air-conditioning control considering fuzzy parameters using immune clonal selection programming," IEEE Transactions on Smart Grid, vol. 3, pp. 1603-1610, 2012.
[18]A. Anvari-Moghaddam, H. Monsef, and A. Rahimi-Kian, "Optimal smart home energy management considering energy saving and a comfortable lifestyle," IEEE Transactions on Smart Grid, vol. 6, pp. 324-332, 2015.
[19]M. Beaudin and H. Zareipour, "Home energy management systems: A review of modelling and complexity," Renewable and Sustainable Energy Reviews, vol. 45, pp. 318-335, 2015.
[20]Z. Wu, S. Zhou, J. Li, and X.-P. Zhang, "Real-time scheduling of residential appliances via conditional risk-at-value," IEEE Transactions on Smart Grid, vol. 5, pp. 1282-1291, 2014.
[21]Y. Iwafune, T. Ikegami, J. G. da Silva Fonseca Jr, T. Oozeki, and K. Ogimoto, "Cooperative home energy management using batteries for a photovoltaic system considering the diversity of households," Energy Conversion and Management, vol. 96, pp. 322-329, 2015.
[22]Y. Wang, B. Wang, C.-C. Chu, H. Pota, and R. Gadh, "Energy management for a commercial building microgrid with stationary and mobile battery storage," Energy and Buildings, vol. 116, pp. 141-150, 2016.
[23]L. Chuan and A. Ukil, "Modeling and validation of electrical load profiling in residential buildings in Singapore," IEEE Transactions on Power Systems, vol. 30, pp. 2800-2809, 2015.
[24]A. Soares, Á. Gomes, C. H. Antunes, and C. Oliveira, "A Customized Evolutionary Algorithm for Multiobjective Management of Residential Energy Resources," IEEE Transactions on Industrial Informatics, vol. 13, pp. 492-501, 2017.
[25]F. Wang, L. Zhou, H. Ren, X. Liu, S. Talari, M. Shafie-khah, et al., "Multi-objective optimization model of source-load-storage synergetic dispatch for building energy system based on TOU price demand response," IEEE Transactions on Industry Applications, 2017.
[26]O. Elma, A. Taşcıkaraoğlu, A. T. İnce, and U. S. Selamoğulları, "Implementation of a dynamic energy management system using real time pricing and local renewable energy generation forecasts," Energy, vol. 134, pp. 206-220, 2017.
[27]A.-H. Mohsenian-Rad and A. Leon-Garcia, "Optimal residential load control with price prediction in real-time electricity pricing environments," IEEE transactions on Smart Grid, vol. 1, pp. 120-133, 2010.
[28]E. Gavanidous and A. Bakirtzis, "Design of a stand alone system with renewable energy sources using trade off methods," IEEE Transactions on Energy Conversion, vol. 7, pp. 42-48, 1992.
[29]F. Lasnier, Photovoltaic engineering handbook: Routledge, 2017.
[30]S. Nistor, J. Wu, M. Sooriyabandara, and J. Ekanayake, "Cost optimization of smart appliances," in Innovative Smart Grid Technologies (ISGT Europe), 2011 2nd IEEE PES International Conference and Exhibition on, 2011, pp. 1-5.
[31]H.-H. Chang, W.-Y. Chiu, and C.-M. Chen, “User-centric multiobjective approach to privacy preservation and energy cost minimization in smarthome,”IEEE Syst. J., vol. 13, no. 1, pp. 1030–1041, Mar. 2019.
[32]J. Kennedy, "Particle swarm optimization," in Encyclopedia of machine learning, ed: Springer, 2011, pp. 760-766.
Journal of Renewable and New Energy
Volume 8, Issue 2 - Serial Number 16
September 2021
Pages 11-20

Files

History

  • Receive Date: 17 March 2020
  • Revise Date: 18 October 2020
  • Accept Date: 10 November 2020

Share

How to cite

Statistics