Abstract
AbstractThe development of distributed renewable energy, such as photovoltaic power and wind power generation, makes the energy system cleaner, and is of great significance in reducing carbon emissions. However, weather can affect distributed renewable energy power generation, and the uncertainty of output brings challenges to uncertainty planning for distributed renewable energy. Energy systems with high penetration of distributed renewable energy involve the high-dimensional, nonlinear dynamics of large-scale complex systems, and the optimal solution of the uncertainty model is a difficult problem. From the perspective of statistical machine learning, the theory of planning of distributed renewable energy systems under uncertainty is reviewed and some key technologies are put forward for applying advanced artificial intelligence to distributed renewable power uncertainty planning.
Funder
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Subject
Electrical and Electronic Engineering,Energy Engineering and Power Technology,Safety, Risk, Reliability and Quality
Reference164 articles.
1. Wang, H., Liu, Y., Zhou, B., Li, C., & Barakhtenko, E. (2020). Taxonomy research of artificial intelligence for deterministic solar power forecasting. Energy Conversion and Management, 214, 112909.
2. Murty, V. V. S. N., & Kumar, A. (2020). Multi-objective energy management in microgrids with hybrid energy sources and battery energy storage systems. Protection and Control of Modern Power Systems, 5(1), 1–20.
3. Liu, Y., Lin, J., Wu, Q. H., & Zhou, X. (2017). Frequency control of DFIG-based wind power penetrated power systems using switching angle controller and AGC. IEEE Transactions on Power Systems, 32(2), 1553–1567.
4. Zheng, J. H., Chen, J., Wu, Q. H., & Jing, Z. (2015). Multi-objective optimization and decision making for power dispatch of a large-scale integrated energy system with distributed DHCs embedded. Applied Energy, 154, 369–379.
5. Shen, X., Shahidehpour, M., Han, Y., Zhu, S., & Zheng, J. (2017). Expansion planning of active distribution networks with centralized and distributed energy storage systems. IEEE Transactions on Sustainable Energy, 8(1), 126–134.
Cited by
47 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献