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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.
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Comparing scenario reduction methods for stochastic transmission planning
Policy, technology, and economic uncertainties affect the net benefits of grid reinforcements, and should be considered in planning. Stochastic optimisation can improve the robustness and expected performance of transmission plans, but is computationally intensive because model size grows as more scenarios are considered. Therefore, the ability to find a small number of scenarios while still capturing the benefits of stochastic programming is crucial. In this study, the authors evaluate the performance of several promising scenario sampling methods. Criteria for comparison include an index of the economic consequences of simplifying scenarios (the expected cost of naïve solution), changes in first‐stage investment decisions, and maximum regret. The results of an application to multidecadal planning of the Western Electricity Coordinating Council system show that solutions perform well when based on scenarios chosen by either a distance‐based method or the stratified scenario section method with moment‐matched probabilities. In particular, for this application, these methods’ results closely resemble solutions obtained from a much larger model using the full scenario set, and surprisingly have a lower worst case regret. Thus, careful scenario reduction can result in useful models that are more easily solved or, alternatively, can be expanded to accommodate other important features of power systems and markets.
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- Award ID(s):
- 1736414
- PAR ID:
- 10570709
- Publisher / Repository:
- DOI PREFIX: 10.1049
- Date Published:
- Journal Name:
- IET Generation, Transmission & Distribution
- Volume:
- 13
- Issue:
- 7
- ISSN:
- 1751-8687
- Format(s):
- Medium: X Size: p. 1005-1013
- Size(s):
- p. 1005-1013
- Sponsoring Org:
- National Science Foundation
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