Optimal Load Scheduling of Home Appliances Considering Operation Conditions
DOI:
https://doi.org/10.31181/oresta121222211jKeywords:
Load scheduling, House appliances scheduling, Smart Home, Mixed-integer linear programming (MILP)Abstract
To reduce energy consumption arising from increasing energy efficiency in response to energy depletion around the world, energy price rises, climate change, and accidents of electric power are cooperating simultaneously. Recognizing the seriousness of the above-mentioned problems, feasible and effective policies for reducing greenhouse gas have been promoted in developed countries since the 2000s. Moreover, industry and academia are actively researching to develop energy-efficient and eco-friendly technologies, respectively. This study proposes an optimal model for scheduling home appliances that minimizes power costs by assuming a smart-home environment with smart metering and advanced metering infrastructure. In addition, a case-study was performed using actual data from South Korea, and sensitivity analysis was performed according to changes in parameters. The experiment considered possible real-life situations, such as an increase and decrease in power cost and a limitation in power usage, and proved that the proposed model was excellent to establish a power schedule for home appliances. This research result seems to serve as a guideline in relation to the control of home appliances to reduce power and smart homes.
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References
Adika, C. O., & Wang, L. (2014). Smart charging and appliance scheduling approaches to demand side management. International Journal of Electrical Power & Energy Systems, 57, 232-240. https://doi.org/10.1016/j.ijepes.2013.12.004
Alham, M., Elshahed, M., Ibrahim, D. K., & El Zahab, E. E. D. A. (2016). A dynamic economic emission dispatch considering wind power uncertainty incorporating energy storage system and demand side management. Renewable Energy, 96, 800-811. https://doi.org/10.1016/j.renene.2016.05.012
Althaher, S., Mancarella, P., & Mutale, J. (2015). Automated demand response from home energy management system under dynamic pricing and power and comfort constraints. IEEE Transactions on Smart Grid, 6(4), 1874-1883. https://doi.org/10.1109/TSG.2014.2388357
Beaudin, M., & Zareipour, H. (2015). Home energy management systems: A review of modelling and complexity. Renewable and sustainable energy reviews, 45, 318-335. https://doi.org/10.1016/j.rser.2015.01.046
Bharathi, C., Rekha, D., & Vijayakumar, V. (2017). Genetic algorithm based demand side management for smart grid. Wireless personal communications, 93(2), 481-502. https://doi.org/10.1007/s11277-017-3959-z
Chakraborty, N., Mondal, A., & Mondal, S. (2020). Efficient load control based demand side management schemes towards a smart energy grid system. Sustainable Cities and Society, 59, 102175. https://doi.org/10.1016/j.scs.2020.102175
Chakraborty, S., Ito, T., Senjyu, T., & Saber, A. Y. (2013). Intelligent economic operation of smart-grid facilitating fuzzy advanced quantum evolutionary method. IEEE Transactions on Sustainable Energy, 4(4), 905-916. https://doi.org/10.1109/TSTE.2013.2256377
Foroozandeh, Z., Ramos, S., Soares, J., & Vale, Z. (2021). Energy management in smart building by a multi-objective optimization model and pascoletti-serafini scalarization approach. Processes, 9(2), 257. https://doi.org/10.3390/pr9020257
Foroozandeh, Z., Ramos, S., Soares, J., Vale, Z., & Dias, M. (2022). Single contract power optimization: A novel business model for smart buildings using intelligent energy management. International Journal of Electrical Power & Energy Systems, 135, 107534. https://doi.org/10.1016/j.ijepes.2021.107534
Gupta, A., Singh, B. P., & Kumar, R. (2016). Optimal provision for enhanced consumer satisfaction and energy savings by an intelligent household energy management system. Paper presented at the 2016 IEEE 6th International Conference on Power Systems (ICPS).
Korea Electric Power Corporation (2021). Basic Terms of Supply Enforcement Detailed Rules.
Korea Power Exchange (2020). Investigation report on the supply status of home appliances in 2019.
Korkas, C. D., Terzopoulos, M., Tsaknakis, C., & Kosmatopoulos, E. B. (2022). Nearly optimal demand side management for energy, thermal, EV and storage loads: An Approximate Dynamic Programming approach for smarter buildings. Energy and Buildings, 255, 111676. https://doi.org/10.1016/j.enbuild.2021.111676
Ma, K., Yao, T., Yang, J., & Guan, X. (2016). Residential power scheduling for demand response in smart grid. International Journal of Electrical Power & Energy Systems, 78, 320-325. https://doi.org/10.1016/j.ijepes.2015.11.099
Morales-España, G., Martínez-Gordón, R., & Sijm, J. (2022). Classifying and modelling demand response in power systems. Energy, 242, 122544. https://doi.org/10.1016/j.energy.2021.122544
Nazemi, S. D., Jafari, M. A., & Zaidan, E. (2021). An incentive-based optimization approach for load scheduling problem in smart building communities. Buildings, 11(6), 237. https://doi.org/10.3390/buildings11060237
Nezhad, A. E., Rahimnejad, A., & Gadsden, S. A. (2021). Home energy management system for smart buildings with inverter-based air conditioning system. International Journal of Electrical Power & Energy Systems, 133, 107230. https://doi.org/10.1016/j.ijepes.2021.107230
Ogunjuyigbe, A., Ayodele, T., & Akinola, O. (2017). User satisfaction-induced demand side load management in residential buildings with user budget constraint. Applied Energy, 187, 352-366. https://doi.org/10.1016/j.apenergy.2016.11.071
Samadi, P., Wong, V. W., & Schober, R. (2015). Load scheduling and power trading in systems with high penetration of renewable energy resources. IEEE Transactions on Smart Grid, 7(4), 1802-1812. https://doi.org/10.1109/TSG.2015.2435708
Shabanzadeh, M., & Moghaddam, M. P. (2013). What is the smart grid? Definitions, perspectives, and ultimate goals. Paper presented at the 28th International Power System Conference (PSC).
Sou, K. C., Weimer, J., Sandberg, H., & Johansson, K. H. (2011). Scheduling smart home appliances using mixed integer linear programming. Paper presented at the 2011 50th IEEE conference on decision and control and European control conference.
Wang, J., Li, Y., & Zhou, Y. (2016). Interval number optimization for household load scheduling with uncertainty. Energy and Buildings, 130, 613-624. https://doi.org/10.1016/j.enbuild.2016.08.082
Zhu, Z., Tang, J., Lambotharan, S., Chin, W. H., & Fan, Z. (2012). An integer linear programming based optimization for home demand-side management in smart grid. Paper presented at the 2012 IEEE PES innovative smart grid technologies (ISGT).
