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Optimal scheduling for the electrical energy consumption of residential buildings in a microgrid considering the priority of operation through IoT infrastructure
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Seyed ali Hosseini    , Mehrdad Hojjat * 1, Azita Azarfar |
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Abstract: (2940 Views) |
| Demand-side response in residential homes is responsible for significant changes in their electricity consumption patterns. Such systems are implemented to shift the load from peak hours to off-peak hours. This approach not only reduces the costs of consumer’s energy bills but also brings about many benefits such as postponing power system planning investments, improving network reliability, reducing unexpected outages, and so on. This paper introduces a new structure for managing the electrical energy consumption of residential homes via power aggregation by considering the consumers’ priorities in a microgrid. In this situation, the performance priorities of the controllable types of equipment are first sent along with the consumption information of the total electrical equipment to the power aggregator unit through a smart meter. After gathering all information from customers, scheduling is done by a power aggregator in which network constraints are considered. Finally, management programs are sent as a series of binary codes directly from the aggregator to the smart sockets with the help of the Internet of things (IoT) infrastructure. In fact, in this project, there is no need to use home energy management systems (HEMSs) for residential homes, and only smart meters are employed to send information. In this method, information is sent directly from the central control unit to the smart sockets using the IoT technology and the process of information by another separate unit is not needed. In other words, consumption planning for all consumer’s controllable devices is coordinated from the power aggregation’s point of view to minimize the energy cost of all consumers by taking the constraints of the distribution network and all consumption priorities into account. In this project, an IEEE standard 15-bus microgrid with 50 households (with the average consumption pattern of a 4-person household for 3 months) is used. For each family, there are 12 electrical devices, in which two of them (dishwasher and washing machine) are considered as controllable appliances, and all planning programs are done for scheduling their operating time. The time horizon is considered 24 hours consisting of 15-min time-steps. To better understand the results in different working conditions, six different scenarios are defined in this regard and the results are compared with each other. Finally, according to the simulation results based on the time of use (TOU) tariff defined for 2019 in Iran, it can be realized that by planning the consumption of controllable types of equipment, 42.1% of peak-load duration cost and 21.8% of the total cost of electricity consumption is saved. |
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| Keywords: Home Appliance Consumption Management, Operating Priority, Internet of Things, Genetic Algorithm. |
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Full-Text [PDF 867 kb]
(498 Downloads)
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Type of Study: Research |
Received: 2020/11/28 | Accepted: 2021/05/15 | Published: 2021/06/27
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1. Hu, M, Xiao, J. W, Cui, S. C, and Wang, Y. W,"Distributed real-time demand response for energy management scheduling in smart grid", International Journal of Electrical Power & Energy Systems, vol. 99, pp. 233-245, 2018. [ DOI:10.1016/j.ijepes.2018.01.016] 2. Ejaz, W, Naeem, M, Shahid, A, Anpalagan, A, and Jo, M. "Efficient energy management for the internet of things in smart cities", IEEE Com. Magazine, vol. 55, no. 1, pp. 84-91, 2017. [ DOI:10.1109/MCOM.2017.1600218CM] 3. Hussain, H. M, Javaid, N, Iqbal, S, Hasan, Q. U, Aurangzeb, K, and Alhussein, M. "An Efficient Demand Side Management system with a New Optimized Home Energy Management Controller in Smart Grid", Energies, vol. 11, no. 1, pp. 190, 2018. [ DOI:10.3390/en11010190] 4. Shakeri, M, Shayestegan, M, Reza, S. S, Yahya, I, Bais, B, Akhtaruzzaman, K, Sopian, K, and Amin, N. "Implementation of a novel home energy management system (HEMS) architecture with solar photovoltaic system as supplementary source", Renewable Energy, vol. 125, pp. 108-120, 2018. [ DOI:10.1016/j.renene.2018.01.114] 5. Lu, X, Zhou, K, Chan, F. T, and Yang, S. "Optimal scheduling of household appliances for smart home energy management considering demand response", Natural Hazards, vol. 88, no. 3, pp. 1639-1653, 2017. [ DOI:10.1007/s11069-017-2937-9] 6. Yoon, S. H. Kim, S. Y. Park, G. H. Kim, Y. K. Cho, C. H. and Park, B. H. "Multiple power-based building energy management system for efficient management of building energy, Sustainable Cities and Society", vol. 42, pp. 462-470, 2018. [ DOI:10.1016/j.scs.2018.08.008] 7. Zhang, c.q. He, b.j. Tang, and y.m. wei. "china's energy consumption in the building sector: a life cycle approach", energy build. vol. 94, pp. 240-251, 2015. [ DOI:10.1016/j.enbuild.2015.03.011] 8. Al-Ali, A.R. Zualkernan, I.A. Rashid, M. Gupta, R. and Alikarar, M. "A smart home energy management system using IoT and big data analytics approach", IEEE Trans. Consum. Electron. vol. 63, no. 4, pp. 426-434, 2017. [ DOI:10.1109/TCE.2017.015014] 9. Babaei, Toktam, Hamid Abdi, Chee Peng Lim, and Saeid Nahavandi. "A study and a directory of energy consumption data sets of buildings", Energy and Buildings 94 (2015): 91-99. [ DOI:10.1016/j.enbuild.2015.02.043] 10. Tang, Samuel, Vineetha Kalavally, Kok Yew Ng, and Jussi Parkkinen. "Development of a prototype smart home intelligent lighting control architecture using sensors onboard a mobile computing device", Energy and buildings 138 (2017): 368-376. [ DOI:10.1016/j.enbuild.2016.12.069] 11. Basit, A. Sidhu, G. A. S. Mahmood, A. and Gao, F. "Efficient and autonomous energy management techniques for the future smart homes", IEEE Transactions on Smart Grid, vol. 8, no. 2, pp. 917-926, 2017. 12. Rastegar, Mohammad, Mahmud Fotuhi-Firuzabad, and Hamidreza Zareipour. "Home energy management incorporating operational priority of appliances", International Journal of Electrical Power & Energy Systems 74 (2016): 286-292. [ DOI:10.1016/j.ijepes.2015.07.035] 13. Fatih Issi; Orhan Kaplan. "The Determination of Load Profiles and Power Consumptions of Home Appliances", Energies 2018, 11, 607; doi:10.3390/en11030607 [ DOI:10.3390/en11030607] 14. https://tbtb.ir/uploads/1398.pdf 15. Logenthiran, T. Srinivasan, D. Khambadkone, A.M. "Multi-agent system for energy resource scheduling of integrated microgrids in a distributed system", Electr. Power Syst. Res. 2011, 81, 138-148. [ DOI:10.1016/j.epsr.2010.07.019] |
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hosseini S A, hojjat M, Azarfar A. Optimal scheduling for the electrical energy consumption of residential buildings in a microgrid considering the priority of operation through IoT infrastructure. ieijqp 2021; 10 (2) :88-95 URL: http://ieijqp.ir/article-1-791-en.html
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