Literature Review on the Advancement of Renewable Energy Technologies in Electrical Engineering
Kata Kunci:
renewable energy, electrical engineering, smart grid, energy storage, artificial intelligenceAbstrak
This literature review explores the advancements in renewable energy technologies within the field of electrical engineering, driven by the increasing demand for sustainable and environmentally friendly energy solutions. Employing a qualitative literature review approach, the study examines scholarly sources from 2015 to 2024 to identify key technical innovations in energy storage systems, power electronics, smart grid integration, and the application of artificial intelligence in energy management. The findings reveal significant improvements in inverter design, battery energy storage systems, and the adoption of Internet of Things (IoT) and Artificial Intelligence (AI), all contributing to enhanced reliability, efficiency, and flexibility of renewable energy systems. Despite these advancements, challenges such as grid synchronization, power fluctuations, and system integration persist, requiring adaptive and innovative engineering solutions. This review highlights the vital role of electrical engineering in accelerating the global energy transition through an interdisciplinary approach that bridges theoretical foundations with technological applications.
Referensi
Kiasari, A. Z., Ghasemi, A., & Karami, H. (2024). A comprehensive review of the current status of smart grid technologies for renewable energies integration and future trends: The role of machine learning and energy storage systems. Renewable and Sustainable Energy Reviews, 189, 114416. https://doi.org/10.1016/j.rser.2024.114416
Nguyen, H. D., Tran, T. Q., & Bui, D. T. (2024). Integrating artificial intelligence in energy transition: Challenges and opportunities. Energy Reports, 10, 102–119. https://doi.org/10.1016/j.egyr.2024.05.012
Rehman, M. A., Hussain, A., & Khan, M. A. (2023). The evolution of AI applications in the energy system transition. Energies, 18(6), 1523. https://doi.org/10.3390/en18061523
Ali, S. M., & Rahman, M. T. (2023). Artificial intelligence and machine learning in renewable and sustainable energy strategies: A critical review and future perspectives. Renewable Energy, 210, 1340–1355. https://doi.org/10.1016/j.renene.2023.04.045
Zhang, Y., Li, M., & Wang, T. (2023). Smart inverters and their role in grid integration of renewable energy systems. International Journal of Electrical Power & Energy Systems, 150, 109011. https://doi.org/10.1016/j.ijepes.2023.109011
Fatima, N., & Raza, M. (2022). AI-based forecasting for renewable energy generation: A review. Energy AI, 10, 100197. https://doi.org/10.1016/j.egyai.2022.100197
Chen, L., Liu, Y., & Zhang, Z. (2019). Battery energy storage systems for grid applications: A review of recent developments. Applied Energy, 240, 821–834. https://doi.org/10.1016/j.apenergy.2019.02.055
Kumar, R., & Singh, A. (2021). Power electronics in wind energy conversion systems: A review. Renewable and Sustainable Energy Reviews, 135, 110131. https://doi.org/10.1016/j.rser.2020.110131
Li, H., & Wang, J. (2023). Optimization of microgrid operation using IoT and SCADA integration. Journal of Cleaner Production, 407, 136827. https://doi.org/10.1016/j.jclepro.2023.136827
Al-Shetwi, A. Q., Hannan, M. A., & Hussain, A. (2020). Grid integration issues of renewable energy sources: A review of recent developments. Electric Power Systems Research, 189, 106602. https://doi.org/10.1016/j.epsr.2020.106602
