Abstract
Microgrids are a critical component of smart and sustainable cities as they provide localized power generation and distribution that can be optimized for efficiency, cost, and environmental impact. Further, many microgrids are characterized for the presence of smart homes, which are an integral part of smart cities as they play a crucial role in improving the overall efficiency and sustainability of urban areas. One of the primary benefits of smart homes is the ability to reduce energy consumption and carbon emissions, for example, by automatically adjusting the temperature, lighting, and other settings to optimize energy usage based on the users’ needs and preferences. However, the efficient management of smart homes located in microgrids is still an open question. In particular, the efficient management of microgrids including smart homes requires measuring and processing a large amount of electrical data related to the energy generated by the power sources of the microgrid, the energy consumed by the loads (home appliances), the level of battery storage and the amount of transferred power flow between the microgrid and the main grid. This paper reflects on how to measure electrical variables at different points of the microgrids using low-cost smart meters with IoT capabilities and how to apply Non-Intrusive Load Monitoring (NILM) methods for energy efficiency purposes. The empirical study involving a real microgrid including a hybrid wind-solar generation system and a set of home appliances show that it is possible to collect data of different parts of the microgrid for efficient management purposes.
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References
Bibri, S.E., Krogstie, J.: Smart sustainable cities of the future: an extensive interdisciplinary literature review. Sustain. Cities Soc. 31, 183–212 (2017)
Pinto-Santos, F., Shoeibi, N., Rivas, A., Hernández, G., Chamoso, P., De La Prieta, F.: Distributed platform for the extraction and analysis of information. In: Corchado, J.M., Trabelsi, S. (eds.) Sustainable Smart Cities and Territories, pp. 200–210. Springer (2022)
Wang, S.J., Moriarty, P.: Energy savings from smart cities: a critical analysis. Energy Procedia 158, 3271–3276 (2019)
Corchado, J.M., Larriba-Pey, J.L., Chamoso-Santos, P., De la Prieta-Pintado, F.: Advances in public transport platform for the development of sustainability cities. Electronics 10(22), 2771 (2021)
Saeed, M.H., Fangzong, W., Kalwar, B.A., Iqbal, S.: A review on microgrids’ challenges & perspectives. IEEE Access 9, 166502–166517 (2021)
Kim, H., Choi, H., Kang, H., An, J., Yeom, S., Hong, T.: A systematic review of the smart energy conservation system: from smart homes to sustainable smart cities. Renew. Sustain. Energy Rev. 140, 110755 (2021)
Mansouri, S.A., Ahmarinejad, A., Nematbakhsh, E., Javadi, M.S., Jordehi, A.R., Catalao, J.: Energy management in microgrids including smart homes: a multi-objective approach. Sustain. Cities Soc. 69, 102852 (2021)
Anvari–Moghaddam, A., Guerrero, J.M., Vasquez, J.C., Monsef, H., Rahimi-Kian, A.: Efficient energy management for a grid–tied residential microgrid. IET Gener., Transm. Distrib. 11(11), 2752–2761 (2017)
Canaan, B., Colicchio, B., Ould-Abdeslam, D.: Microgrid cyber-security: review and challenges toward resilience. Appl. Sci. 10(16), 5649 (2020)
Ridi, A., Gisler, C., Hennebert, J.: A survey on intrusive load monitoring for appliance recognition. In: Proceedings of 22nd International Conference on Pattern Recognition, pp. 3702–3707. IEEE, Stockholm (Sweden) (2014)
Hart, G.W.: Nonintrusive appliance load monitoring. Proc. IEEE 80(12), 1870–1891 (1992)
Alcalá, J., Ureña, J., Hernández, A., Gualda, D.: Event-based energy disaggregation algorithm for activity monitoring from a single-point sensor. IEEE Trans. Instrum. Meas. 66(10), 2615–2626 (2017)
Viciana, E., Alcayde, A., Montoya, F.G., Baños, R., Arrabal-Campos, F.M., Manzano-Agugliaro, F.: An open hardware design for internet of things power quality and energy saving solutions. Sensors 19(3), 627 (2019)
Ghosh, S., Chatterjee, A., Chatterjee, D.: An improved load feature extraction technique for smart Homes using fuzzy-based NILM. IEEE Trans. Instrum. Meas. 70, 1–9 (2021)
Isanbaev, V., Baños, R., Arrabal-Campos, F.M., Gil, C., Montoya, F.G., Alcayde, A.: A comparative study on pretreatment methods and dimensionality reduction techniques for energy data disaggregation in home appliances. Adv. Eng. Inform. 54, 101805 (2022)
Aslam, S., Herodotou, H., Mohsin, S.M., Javaid, N., Ashraf, N., Aslam, S.: A survey on deep learning methods for power load and renewable energy forecasting in smart microgrids. Renew. Sustain. Energy Rev. 144, 110992 (2021)
González-Briones, A., Hernandez, G., Corchado, J.M., Omatu, S., Mohamad, M.S.: Machine learning models for electricity consumption forecasting: a review. In: 2019 2nd International Conference on Computer Applications & Information Security (ICCAIS), pp. 1–6. IEEE (2019)
Baños, R., Gil, C., Agulleiro, J.I., Reca, J.: A memetic algorithm for water distribution network design. In: Saad et al. (eds.) Soft Computing in Industrial Applications: Recent Trends, pp. 279–289. Springer (2007)
Dashtdar, M., Flah, A., Hosseinimoghadam, S.M.S., El-Fergany, A.: Frequency control of the islanded microgrid including energy storage using soft computing. Sci. Rep. 12(1), 20409 (2022)
Acknowledgements
This work was supported by project PGC2018-098813-B-C33 (Spanish “Ministerio de Ciencia, Innovación y Universidades”), and by European Regional Development Funds (ERDF).
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Isanbaev, V., Baños, R., Gil, C., Gil, M.M., Martínez, F., Alcayde, A. (2023). Towards Energy Efficiency in Microgrids for Smart Sustainable Cities. In: Castillo Ossa, L.F., Isaza, G., Cardona, Ó., Castrillón, O.D., Corchado Rodriguez, J.M., De la Prieta Pintado, F. (eds) Trends in Sustainable Smart Cities and Territories . SSCT 2023. Lecture Notes in Networks and Systems, vol 732. Springer, Cham. https://doi.org/10.1007/978-3-031-36957-5_22
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DOI: https://doi.org/10.1007/978-3-031-36957-5_22
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