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Warianty tytułu
Języki publikacji
Abstrakty
This paper presents the application of a transportation algorithm to optimize energy flow within a smart grid context. By leveraging this well-established optimization technique, it is demonstrated that energy efficiency can be enhanced and costs lowered in individual households equipped with smart appliances and connected to both traditional and renewable energy sources. Simulation studies have shown that the algorithm can effectively determine optimal energy consumption patterns, leading to significant energy savings. Additionally, the algorithm can provide valuable insights into network congestion and energy demand forecasting, enabling distribution system operators to make informed decisions. The proposed solution aligns well with the concept of smart homes. By integrating with smart devices, such as smart sockets and thermostats, energy consumption can be optimized based on real-time pricing and renewable energy availability, ultimately leading to lower energy bills and increased user comfort. Extending this approach to the distribution network level, by applying the transportation algorithm to optimize energy flow at the medium and low voltage levels, could further enhance grid stability and facilitate the integration of renewable energy sources.
Czasopismo
Rocznik
Tom
Strony
1--16
Opis fizyczny
Bibliogr. 22 poz., rys., wykr., wzory
Twórcy
autor
- University of Zielona Góra, Institute of Metrology, Electronics and Computer Science, ul. prof. Z. Szafrana 2, 65-516 Zielona Góra, Poland
Bibliografia
- [1] Masoud, F., Azah, M., Hussain, S., & Hadi, Z. (2013). Power quality impacts of high-penetration electric vehicle stations and renewable energy-based generators on power distribution systems. Measurement, 46(8), 2423-2434. https://doi.org/10.1016/j.measurement.2013.04.032
- [2] Prieto-Herráez, D., Martínez-Lastras, S., Frías-Paredes, L., Asensio, M. I., & González-Aguilera, D. (2024). EOLO, a wind energy forecaster based on public information and automatic learning for the Spanish Electricity Markets. Measurement, 231, 114557. https://doi.org/10.1016/j.measurement.2024.114557
- [3] Płachta, K., Mroczka, J., & Ostrowski, M. (2023). A new approach to water cooling of photovoltaic panels with a tracking system. Metrology and Measurement Systems, 30(4), 675-687. https://doi.org/10.24425/mms.2023.147961
- [4] Walczak, M., Bychto, L., Kraśniewski, J., & Duer, S. (2022). Design and evaluation of a low-cost solar simulator and measurement system for low-power photovoltaic panels. Metrology and Measurement Systems, 29(4), 685-700. https://doi.org/10.24425/mms.2022.143067
- [5] Mesbahi, O., Tlemçani, M., Janeiro, F.M., Hajjaji, A., & Kandoussi, K. (2021). Sensitivity analysis of a new approach to photovoltaic parameters extraction based on the total least squares method. Metrology and Measurement Systems, 28(4), 751-765. https://doi.org/10.24425/mms.2021.137707
- [6] Moloudian, G., Hosseinifard, M., Kumar, S., Simorangkir, R.B., Buckley, J.L., Song, C., . . . , O’Flynn, B. (2024). RF Energy Harvesting Techniques for Battery-Less Wireless Sensing, Industry 4.0, and Internet of Things: A Review. IEEE Sensors Journal, 24(5), 5732-5745. https://doi.org/10.1109/JSEN.2024.3352402
- [7] Dehghan Hamani, I., Tikani, R., Assadi, H., & Ziaei-Rad, S. (2020). Energy harvesting from moving harmonic and moving continuous mass traversing on a simply supported beam. Measurement, 150, 107080. https://doi.org/10.1016/j.measurement.2019.107080
- [8] Bouřa, A. (2020). A simple and affordable powering circuit for IoT sensor nodes with energy harvesting. Metrology and Measurement Systems, 27(4), 575-587. https://doi.org/10.24425/mms.2020.134839
- [9] Medina-Gracia, R., de Castro, A. d., Garrido-Zafra, J., Moreno-Munoz, A., & Cañete-Carmona, E. (2019). Power Quality Sensor for Smart Appliance’s Self-Diagnosing Functionality. IEEE Sensors Journal, 19(20), 9486-9495. https://doi.org/10.1109/JSEN.2019.2924574
- [10] Ghosh, S., Manna, D., Chatterjee, A., & Chatterjee, D. (2021). Remote Appliance Load Monitoring and Identification in a Modern Residential System with Smart Meter Data. IEEE Sensors Journal, 21(4), 5082-5090. https://doi.org/10.1109/JSEN.2020.3035057
- [11] Radej, B., Drnovšek, J., & Begeš, G. (2019). Effect of environmental and operating conditions on the verification interval for smart electronic electricity meters. Metrology and Measurement Systems, 26(1), 171-184. https://doi.org/10.24425/mms.2019.126328
- [12] Koech, R., Gyasi-Agyei, Y., & Randall, T. (2018). The evolution of urban water metering and conservation in Australia. Flow Measurement and Instrumentation, 62, 19-26. https://doi.org/10.1016/j.flowmeasinst.2018.03.011
- [13] Qi, R., Zheng, J., Luo, Z., & Li, Q. (2022). A Novel Unsupervised Data-Driven Method for Electricity Theft Detection in AMI Using Observer Meters. IEEE Transactions on Instrumentation and Measurement, 71, 1-10. https://doi.org/10.1109/TIM.2022.3189748
- [14] Rincón, A.E., Melo, W.S., de Farias, C.M., & Carmo, L.F. (2021). Securing Smart Meters Through Physical Properties of Their Components. IEEE Transactions on Instrumentation and Measurement, 70, 1-11. https://doi.org/10.1109/TIM.2020.3041098
- [15] Klusacek, J., Langella, R., Meyer, J., & Drapela, J. (2024). Performance of Smart Revenue Meters under Bidirectional Active Energy Flows in Energy Communities. IEEE Transactions on Instrumentation and Measurement, 73, 1-12. https://doi.org/10.1109/TIM.2024.3382732
- [16] Wang, Z., Yu, P., & Zhang, H. (2023). Privacy-Preserving Regulation Capacity Evaluation for HVAC Systems in Heterogeneous Buildings Based on Federated Learning and Transfer Learning. IEEE Transactions on Smart Grid, 14(5), 3535-3549. https://doi.org/10.1109/TSG.2022.3231592
- [17] Powroźnik, P., & Szcześniak, P. (2024). Energy Management of Home Devices with Smart Response for the Energy Generation Profile. IEEE Transactions on Industrial Informatics, 20(4), 6995-7007. https://doi.org/10.1109/TII.2024.3353850
- [18] Barszcz, T., & Zabaryłło, M. ( 2022). Automatic identification of malfunctions of large turbomachinery during transient states with genetic algorithm optimization. Metrology and Measurement Systems, 29(1), 175-190. https://doi.org/10.24425/mms.2022.138551
- [19] Flynn, C., Pengwah, A., Razzaghi, R., & Lachlan, A. L. (2023). An Improved Algorithm for Topology Identification of Distribution Networks Using Smart Meter Data and Its Application for Fault Detection. IEEE Transactions on Smart Grid, 14(5), 3850-3861. https://doi.org/10.1109/TSG.2023.3239650
- [20] Teodorovic, D., & Janic, M. (2022). Transportation Engineering: Theory, Practice, and Modeling. Elsevier Science. https://doi.org/10.1016/C2020-0-03714-X
- [21] Ding, M., Yue, S., Song, K., & Wang, H. (2018). Fuzzy optimal solution of electric tomography imaging: Modelling and application. Flow Measurement and Instrumentation, 59, 72-78. https://doi.org/10.1016/j.flowmeasinst.2017.11.012
- [22] Li, H., Luo, Y., & Ai, D. (2023). Restoration of electromechanical admittance signature via solving constrained optimization problems for concrete structural damage identification. Measurement, 214, 112803. https://doi.org/10.1016/j.measurement.2023.112803
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-a86adec8-1e78-4adf-bb9a-da021327c83b
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