Fuzzy Clustering Based Scenario Reduction for Stochastic Day-Ahead Scheduling in Power Systems

Scenario based stochastic scheduling has drawn a tremendous amount of interest worldwide in tackling the uncertainty of renewable energy and accounting for risks. It is important to generate representative time-series scenarios of renewable energy, while keeping the dimensionality of the scenario set tractable. This paper presents a mixed autoencoder based fuzzy clustering approach to select a reduced scenario set from high-dimensional time series. In contrast to other techniques targeting on minimizing different probability distances, the proposed architecture accounts for the pattern recognition within a large set of scenarios. The effectiveness of the model is verified in the case studies.