Gururaj Bhat, RPTU

Title: Electricity market nodeling

Abstract:

Electricity is a unique and indispensable commodity that underpins nearly every aspect of
modern life. Electricity prices are affected by numerous factors like fuel sources for power
generation, infrastructure of the electricity grids, and weather, to name a few. Modelling
electricity prices is a complex process due to its non-storable property and the different
characteristics it exhibits, which are discussed in this work. Nonetheless, it is important
to have reliable price simulations to manage risks, keep the system operating smoothly,
and encourage investment in infrastructure and technology for the future. Acknowledging
the past work on anomaly detection in electricity price data, this thesis evaluates existing
methods on recent German market prices and addresses the challenges faced by them.
This work proposes a segmented anomaly detection approach for data processing before
the data is used for calibrating the spot price model. A two-factor electricity spot price
model with a diffusion factor and a jump factor is implemented. Analysis and inferences
drawn from studying the impact of the existing and new methods on the data quality are
presented first. The yield of the respective methods is then evaluated by the calibration
and simulation performance of the chosen two-factor model. The results support the
application of segmented approaches presented in this work as they identify jumps with
higher accuracy, and are able to capture more realised spot price observations in the
different quantile ranges of the simulated price paths.