An Efficient Power Scheduling Scheme for Residential Load Management in Smart Homes
Source abstract
Abstract
In this paper, we propose mathematical optimization models of household energy units to optimally control the major residential energy loads while preserving the user preferences. User comfort is modelled in a simple way, which considers appliance class, user preferences and weather conditions. The wind-driven optimization (WDO) algorithm with the objective function of comfort maximization along with minimum electricity cost is defined and implemented. On the other hand, for maximum electricity bill and peak reduction, min-max regret-based knapsack problem (K-WDO) algorithm is used. To validate the effectiveness of the proposed algorithms, extensive simulations are conducted for several scenarios. The simulations show that the proposed algorithms provide with the best optimal results with a fast convergence rate, as compared to the existing techniques.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Smart Grid Energy Management · Microgrid Control and Optimization · Energy Load and Power Forecasting
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
COMSATS Institute of Information Technology, Islamabad 44000, Pakistan
Internetworking Program, Faculty of Engineering, Dalhousie University, Halifax, Nova Scotia B3J 4R2, Canada
Computer Information Science (CIS), Higher Colleges of Technology, Fujairah Campus 4114, The United Arab Emirates (UAE)
University of Alberta, Edmonton, Alberta T6G 2J8, Canada
College of Applied Medical Sciences, Department of Biomedical Technology, King Saud University, Riyadh 11633, Saudi Arabia
References (49)
Show source-provided references
1. Energy Reports. Available online:http://www.enerdata.net/enerdatauk/press-and-publication/energy-features/enerfuture-2007.php.
2. Grid of the future10.1109/MPE.2008.931384 ↗
3. Assessment of Demand Response and Advanced Metering, Available online:http://www.ferc.gov/legal/staff-reports/2010-dr-report.pdf.
4. Greencharge: Managing renewableenergy in smart buildings10.1109/JSAC.2013.130711 ↗
5. Optimal residential load control with price prediction in real-time electricity pricing environments10.1109/TSG.2010.2055903 ↗
6. A Survey on Demand Response Programs in Smart Grids: Pricing Methods and Optimization Algorithms10.1109/COMST.2014.2341586 ↗
7. Xiong, G., Chen, C., Kishore, S., and Yener, A. (2011, January 17–19). Smart (in-home) power scheduling for demand response on the smart grid. Proceedings of the IEEE PES Innovative Smart Grid Technologies Conference, Anaheim, CA, USA.
8. Wireless sensor networks for cost-efficient residential energy management in the smart grid10.1109/TSG.2011.2114678 ↗
9. Samadi, P., Mohsenian-Rad, A., Schober, R., Wong, V.W.S., and Jatskevich, J. (2010, January 4–6). Optimal real-time pricing algorithm based on utility maximization for smart grid. Proceedings of the 1st IEEE International Conference on Smart Grid Communications (SmartGridComm), Gaithersburg, MD, USA.10.1109/SMARTGRID.2010.5622077 ↗
10. The future-oriented grid-smart grid
11. Agrawal, P. (2006, January 23). Overview of DOE microgrid activities. Proceedings of the Symposium on Microgrids, Montreal, QC, Canada. Available online:http://der.lbl.gov/2006 microgrids_files/USA/Presentation_7_Part1_Poonumgrawal.pdf.
12. Shahidehpour, M., Yamin, H., and Li, Z. (2002). Market Operations in Electric Power Systems: Forecasting, Scheduling, and Risk Management, Wiley-IEEE Press.10.1002/047122412X ↗
13. Popovic, Z.N., and Popovic, D.S. (2003, January 23–26). Direct load control as a market-based program in deregulated power industries. Proceedings of the IEEE Bologna Power Tech Conference, Bologna, Italy.
14. Direct load control-A profit-based load management using linear programming10.1109/59.667401 ↗
15. Algorithms for a spot price responding residential load controller10.1109/59.193823 ↗
16. A practical approach to appliance load control analysis: A water heater case study10.1109/TPAS.1983.317816 ↗
17. A linear programming model for reducing system peak through customer load control programs10.1109/59.544648 ↗
18. Coordinated scheduling of residential distributed energy resources to optimize smart home energy services10.1109/TSG.2010.2053053 ↗
19. An intelligent home energy management system to improve demand response10.1109/TSG.2012.2235088 ↗
20. Multi-objective optimal energy consumption scheduling in smart grids10.1109/TSG.2012.2214068 ↗
21. Modified particle swarm optimization applied to integrated demand response and DG resources scheduling10.1109/TSG.2012.2235866 ↗
22. Real-time demand response model10.1109/TSG.2010.2078843 ↗
23. Real-time price-based demand response management for residential appliances via stochastic optimization and robust optimization10.1109/TSG.2012.2212729 ↗
24. Hurtado, L.A., Nguyen, P.H., and Kling, W.L. (2014, January 18–22). Multiple objective Particle Swarm Optimization approach to enable smart buildings-smart grids. Proceedings of the Power Systems Computation Conference (PSCC), Wroclaw, Poland.10.1109/PSCC.2014.7038381 ↗
25. GTES: An optimized game-theoretic demand-side management scheme for smart grid10.1109/JSYST.2013.2260934 ↗
26. Dependable demand response management in the smart grid: A Stackelberg game approach10.1109/TSG.2012.2223766 ↗
27. Ibars, C., Navarro, M., and Giupponi, L. (2010, January 4–6). Distributed demand management in smart grid with a congestion game. Proceedings of the 1st IEEE International Conference on Smart Grid Communications (SmartGridComm), Gaithersburg, MD, USA.10.1109/SMARTGRID.2010.5622091 ↗
28. Scalable and robust demand response with mixed-integer constraints10.1109/TSG.2013.2257893 ↗
29. A scalable three-step approach for demand side management of plug-in hybrid vehicles10.1109/TSG.2012.2213847 ↗
30. A communication-based appliance scheduling scheme for consumerpremise energy management systems10.1109/TSG.2012.2224388 ↗
31. Wang, C., and de Groot, M. (2010, January 13–15). Managing end-user preferences in the smart grid. Proceedings of the 1st ACM International Conference on Energy-Efficient Computing and Networking, Passau, Germany.10.1145/1791314.1791330 ↗
32. Residential load control: Distributed scheduling and convergence with lost AMI messages10.1109/TSG.2011.2176518 ↗
33. A method for designing customer-oriented demand response aggregation service10.1016/j.cirp.2014.03.117 ↗
34. Liu, B., and Wei, Q. (2013, January 16–18). Home energy control algorithm research based on demand response programs and user comfort. Proceedings of the International Conference on Measurement, Information and Control (ICMIC), Harbin, China.
35. Joint optimization of electric vehicle and home energy scheduling considering user comfort preference10.1109/TSG.2013.2274521 ↗
36. Optimal residential load control with price prediction in real-time electricity pricing environments10.1109/TSG.2010.2055903 ↗
37. Residential implementation of critical-peak pricing of electricity10.1016/j.enpol.2006.06.019 ↗
38. Silvano, M., and Toth, P. (1990). Knapsack Problems: Algorithms and Computer Implementations, John Wiley and Sons, Inc.
39. Kellerer, H., Pferschy, U., and Pisinger, D. (2004). Introduction to NP-Completeness of Knapsack Problems, Springer.10.1007/978-3-540-24777-7 ↗
40. Haupt, R.L., and Werner, D.H. (2007). Genetic Algorithms in Electromagnetics, Wiley-IEEE Press.10.1002/047010628X ↗
41. Price, K., Storn, R.M., and Lampinen, J.A. (2005). Differential Evolution: A Practical Approach to Global Optimization, Springer-Verlag.
42. Kennedy, J., and Eberhart, R. (1995, January 20–23). Particle swarm optimization. Proceedings of the 9th International Conference of Neural Networkss, Doha, Qatar.
43. Dorigo, M., and Stutzle, T. (2004). Ant Colony Optimization, MIT Press.10.7551/mitpress/1290.001.0001 ↗
44. Cuckoo search. Available online:https://en.wikipedia.org/wiki/Cuckoo_search.
45. No free lunch theorems for optimization10.1109/4235.585893 ↗
46. The wind driven optimization technique and its application in electromagnetics10.1109/TAP.2013.2238654 ↗
47. Bayraktar, Z., Komurcu, M., and Werner, D.H. (2010, January 11–17). Wind Driven Optimization (WDO): A novel nature-inspired optimization algorithm and its application to electromagnetics. Proceedings of the Antennas and Propagation Society International Symposium (APSURSI), Toronto, AB, Canada.10.1109/APS.2010.5562213 ↗
48. Evolutionary programming made faster10.1109/4235.771163 ↗
49. Wholesale Solar Home Page. Available online:http://www.wholesalesolar.com/.