Abstract
Generation of realistic scenarios is an important prerequisite for analyzing the reliability of renewable-rich power systems. This paper satisfies this need by presenting an end-to-end model-free approach for creating representative power system scenarios on a seasonal basis. A conditional recurrent generative adversarial network serves as the main engine for scenario generation. Compared to prior scenario generation models that treated the variables independently or focused on short-term forecasting, the proposed implicit generative model effectively captures the cross-correlations that exist between the variables considering long-term planning. The validity of the scenarios generated using the proposed approach is demonstrated through extensive statistical evaluation and investigation of end-application results. It is shown that analysis of abnormal scenarios, which is more critical for power system resource planning, benefits the most from cross-correlated scenario generation.
Original language | English (US) |
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Article number | 1636 |
Journal | Energies |
Volume | 16 |
Issue number | 4 |
DOIs | |
State | Published - Feb 2023 |
Externally published | Yes |
Keywords
- dynamic time warping
- generative adversarial network
- power system planning
- renewable energy
- scenario generation
ASJC Scopus subject areas
- Control and Optimization
- Energy (miscellaneous)
- Engineering (miscellaneous)
- Energy Engineering and Power Technology
- Electrical and Electronic Engineering
- Fuel Technology
- Renewable Energy, Sustainability and the Environment