AUTHOR=Xu Ning Zhou , Gao Xiang , Chai Songjian , Niu Ming , Yang Jia Xin 

TITLE=Leveraging sanitized data for probabilistic electricity market prediction: a Singapore case study

JOURNAL=Frontiers in Energy Research

VOLUME=Volume 11 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/energy-research/articles/10.3389/fenrg.2023.1296957

DOI=10.3389/fenrg.2023.1296957

ISSN=2296-598X

ABSTRACT=In deregulated electricity markets, predicting price and load is a common practice. However, market participants and share-holders often seek deeper insights into other system statuses associated with price prediction, such as power flow and market share of generation companies (GenCos). These insights are challenging to obtain using purely data-driven methods. This paper proposes a physicsbased solution for probabilistic pre-diction of market clearing outcomes, using real sanitized offer data from the National Electricity Market of Singapore (NEMS).Our approach begins with approximating the generator of-fers that have historically been cleared. Using this pool of offer data, we propose a probabilistic market clearing process. This process allows for the probabilistic prediction of market prices. By considering the power system network and its constraints, we also naturally obtain probabilistic predictions of power flow and market shares.We validate our approach using actual NEMS data. Our findings show that while the overall performance of price pre-diction is comparable to existing methods, our proposed method can also provide probabilistic predictions of other as-sociated system operating conditions. Furthermore, our meth-od enables scenario studies, such as the impact of demand-side participation and the penetration of rooftop Photovoltaic (PV) systems on the Uniform Singapore Energy Price (USEP).