Method for Wind–Solar–Load Extreme Scenario Generation Based on an Improved InfoGAN
Source abstract
Abstract
In recent years, extreme events have frequently occurred, and the extreme uncertainty of the source-demand side of high-ratio renewable energy systems poses a great challenge to the safe operation of power systems. Accurately generating extreme scenarios related to the source-demand side under a high percentage of new power systems is vital for the safe operation of power systems and the assessment of their reliability. However, at this stage, methods for extreme scenario generation that fully consider the correlation between wind power, solar power, and load are lacking. To address these problems, this paper proposes a method for extreme scenario generation based on information-maximizing generative adversarial networks (InfoGANs) for high-proportion renewable power systems. The example analysis shows that the method for extreme scenario generation proposed in this paper can fully explore the correlation between historical wind–solar–load data, greatly improve the accuracy with which extreme scenarios are generated, and provide effective theories and methodologies for the safe operation of a new type of power system.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Energy Load and Power Forecasting · Power Systems and Renewable Energy · Solar Radiation and Photovoltaics
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
State Key Laboratory of Power Transmission Equipment Technology, Chongqing University, Chongqing 400044, China
State Grid Hebei Electric Power Research Institute, Shijiazhuang 050021, China
Funding
State Grid Electric Power Company
References (29)
Show source-provided references
1. Research on distribution network planning in the context of integrated energy
2. A classical scenario set generation algorithm for wind power/solar based on Wasserstein distance and improved K-medoids clustering
3. Research on source-load scene generation method based on AM-GAN
4. Deep embedded clustering-based water-optical load uncertainty source scene generation method
5. Building air conditioning load scenario generation method based on conditional time series generation adversarial network
6. A monthly wind power scenario analysis method based on time-series generative adversarial network
7. Conditional Style-Based Generative Adversarial Networks for Renewable Scenario Generation10.1109/TPWRS.2022.3170992 ↗
8. Short-term optimal operation of hydro-wind-solar hybrid system with improved generative adversarial networks10.1016/j.apenergy.2019.04.090 ↗
9. A methodology to generate statistically dependent wind speed scenarios10.1016/j.apenergy.2009.09.022 ↗
10. LSTM auto-encoder based representative scenario generation method for hybrid hydro-PV power system10.1049/iet-gtd.2020.0757 ↗
11. Probabilistic solar power forecasting based on weather scenario generation10.1016/j.apenergy.2020.114823 ↗
12. Optimal configuration of concentrating solar power in multi-energy power systems with an improved variational autoencoder10.1016/j.apenergy.2020.115124 ↗
13. A deep generative model for probabilistic energy forecasting in power systems: Normalizing flows10.1016/j.apenergy.2021.117871 ↗
14. Toward optimal risk-averse configuration for HESS with CGANs-based PV scenario generation10.1109/TSMC.2019.2905776 ↗
15. Day-ahead renewable scenario forecasts based on generative adversarial networks10.1002/er.6340 ↗
16. Zhou, H., Mao, Z., Gao, Y., Luo, S., and Sun, Y. (2022, January 8–11). Renewable energy scenario generation method based on order-preserving Wasserstein distance. Proceedings of the 2022 IEEE/IAS Industrial and Commercial Power System Asia (I&CPS Asia), Shanghai, China.10.1109/ICPSAsia55496.2022.9949973 ↗
17. Peng, Y., Ye, L., Zhao, Y., Li, Z., Wang, X., and Li, P. (2022, January 8–11). Stochastic scenario generation for wind power and photovoltaic system based on CGAN. Proceedings of the 2022 IEEE/IAS Industrial and Commercial Power System Asia (I&CPS Asia), Shanghai, China.10.1109/ICPSAsia55496.2022.9949945 ↗
18. He, G., Liu, K., Wang, S., Lei, Y., and Li, J. (2022). CWM-CGAN Method for Renewable Energy Scenario Generation Based on Weather Label Multi-Factor Definition. Processes, 10.10.3390/pr10030470 ↗
19. Renewable scenario generation using stable and controllable generative adversarial networks with transparent latent space
20. Privacy-Preserving Spatiotemporal Scenario Generation of Renewable Energies: A Federated Deep Generative Learning Approach10.1109/TII.2021.3098259 ↗
21. Financial scenario generation for stochastic multi-stage decision processes as facility location problems10.1007/s10479-006-0140-6 ↗
22. Extreme weather events on energy systems: A comprehensive review on impacts, mitigation, and adaptation measures10.1186/s40807-023-00097-6 ↗
23. Entriken, R., and Lordan, R. (2012, January 22–26). Impacts of extreme events on transmission and distribution systems. Proceedings of the 2012 IEEE Power and Energy Society General Meeting, San Diego, CA, USA.10.1109/PESGM.2012.6345755 ↗
24. Climate Change and the Vulnerability of Germany’s Power Sector to Heat and Drought10.5547/01956574.43.3.agol ↗
25. Meteorological drought effect for Central Europe’s hydropower potential10.1016/j.rser.2023.114175 ↗
26. An ordered curtailment strategy for offshore wind power under extreme weather conditions considering the resilience of the grid10.1109/ACCESS.2019.2911702 ↗
27. Projected impacts of climate change on wind energy density in the United States10.1016/j.renene.2015.06.005 ↗
28. Study on Extreme Output Characteristics of Renewable Energy and Typical Power and Energy Balance Scenarios
29. Zhao, Z., Zhao, C., Li, X., Chen, Z., Pan, Z., and Liu, S. (2023, January 15–17). Refinement Generation Method of Renewable Energy Scenario Based on Information Maximizing Generative Adversarial Network. Proceedings of the 2023 3rd International Conference on Energy, Power and Electrical Engineering (EPEE), Wuhan, China.10.1109/EPEE59859.2023.10351836 ↗