Cross-Correlated Scenario Generation for Renewable-Rich Power Systems Using Implicit Generative Models

Author:

Dalal Dhaval1,Bilal Muhammad1ORCID,Shah Hritik1,Sifat Anwarul Islam1ORCID,Pal Anamitra1ORCID,Augustin Philip2

Affiliation:

1. School of Electrical, Computer, and Energy Engineering, Arizona State University, Tempe, AZ 85281, USA

2. Salt River Project (SRP), 6504 East Thomas Road, Scottsdale, AZ 85251, USA

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.

Funder

Salt River Project

National Science Foundation

Publisher

MDPI AG

Subject

Energy (miscellaneous),Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment,Electrical and Electronic Engineering,Control and Optimization,Engineering (miscellaneous),Building and Construction

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